Research ArticleIdentification of Gastric Cancer-Related Circular RNA throughMicroarray Analysis and Bioinformatics Analysis
Wei Gu1 Ying Sun1 Xiong Zheng1 Jin Ma1 Xiao-Ying Hu1
Tian Gao 2 andMei-Jie Hu 1
1Department of Gastroenterology Ruijin Hospital Luwan Branch Shanghai Jiaotong University School of Medicine Shanghai China2Department of Geriatrics Renji Hospital Shanghai Jiaotong University School of Medicine Shanghai China
Correspondence should be addressed to Tian Gao gaotianmedmailcomcn and Mei-Jie Hu mjhu0119126com
Received 5 July 2017 Accepted 13 December 2017 Published 18 March 2018
Academic Editor Akira Hara
Copyright copy 2018 Wei Gu et al This is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited
Gastric cancer is one of the commonmalignant tumors worldwide Increasing studies have indicated that circular RNAs (circRNAs)play critical roles in the cancer progression and have shown great potential as useful markers and therapeutic targets However theprecise mechanism and functions of most circRNAs are still unknown in gastric cancer In the present study we performed amicroarray analysis to detect circRNA expression changes between tumor samples and adjacent nontumor samples The miRNAexpression profiles were obtained from the National Center of Biotechnology Information Gene Expression Omnibus (GEO) Thedifferentially expressed circRNAs and miRNAs were identified through fold change filtering The interactions between circRNAsand miRNAs were predicted by Arraystarrsquos home-made miRNA target prediction software After circRNA-related miRNAs anddysregulated miRNAs were intersected 23 miRNAs were selected The target mRNAs of miRNAs were predicted by TarBasev70 Gene ontology (GO) enrichment analysis and pathway analysis were performed using standard enrichment computationalmethods for the target mRNAs The results of pathway analysis showed that p53 signaling pathway and hippo signal pathwaywere significantly enriched and CCND2 was a cross-talk gene associated with them Finally a circRNA-miRNA-mRNA regulationnetwork was constructed based on the gene expression profiles and bioinformatics analysis results to identify hub genes andhsa circRNA 101504 played a central role in the network
1 Introduction
Gastric cancer is one of the commonmalignant tumors in theclinic with the second leading cause of cancer-related deathworldwide [1 2] Although the treatment of gastric cancerhas gradually improved cure rate of gastric cancer is still lowWith a lack of obvious clinical symptoms at early stage mostpatients have lost the opportunity of surgical therapy whengastric cancer is detected at advanced stage [3] Recurrenceis the chief cause of gastric cancer-related death Accordingto recent statistics more than 30 of patients sufferingfrom stage III gastric cancer who undergo surgical resectiondevelop recurrence or distant metastasis with a 14-monthmedian recurrence-free survival time [4 5] Therefore pre-vention and cure of gastric cancer are still challenged in theclinic and the search for new molecular markers to monitorand intervene in gastric cancer carcinogenesis is urgent
Recent research has shown that circular RNAs (circR-NAs) play a critical role in the initiation and progression ofhuman diseases especially in tumors and may function aspotential molecular markers for disease diagnosis and treat-ment [6] Circular RNAs are a class of endogenous noncodingRNAs characterized by their covalently closed-loop struc-tures without a 51015840 cap or a 31015840 Poly(A) tail Previous studiesdemonstrated that circRNAs existed widely in all kinds oforganizations [7ndash9] and played a strong regulatory functionin cancer [10 11] For example circRNAs are dysregulated inepithelial tumors such as laryngeal cancer and digestive sys-tem cancers [12ndash14] and in stromal tumors such as gliomas[15] Compared with mRNAs circRNAs are more stable dueto the existence of ring structure Thus circRNA can servesas a convenient tool for qRT-PCR measurements in cancer
CircRNAs have been investigated for more than 40 years[16] but they have been considered as a result of splicing
HindawiBioMed Research InternationalVolume 2018 Article ID 2381680 9 pageshttpsdoiorg10115520182381680
2 BioMed Research International
errors for several decades and their biological functions arelargely unknown With the development of RNA sequenc-ing (RNA-seq) technologies and bioinformatics circRNAshave been extensively explored in recent years and severalfunctions of circRNA have been revealed such as acting asscaffolds in the assembly of protein complexes [17] seques-tering proteins from their native subcellular localization [18]modulating the expression of parental genes [19] regulatingalternative splicing [20] and RNA-protein interactions [21]and functioning as microRNA (miRNA) sponges [8]
Our study aimed to establish the expression profile of gas-tric cancer through circRNA microarray chip detection Ourresults revealed the potential role of circRNAs in gastric can-cer We also aimed to identify the hub circRNAs involved ingastric cancer through bioinformatics analysis The miRNAexpression profiles from the National Center of Biotech-nology Information Gene Expression Omnibus (GEO) wereused to identify circRNA-related dysregulated miRNAs ingastric cancer Gene ontology (GO) enrichment analysis andpathway analysis revealed the potential biology functionof miRNA target genes Finally a circRNA-miRNA-mRNAregulation networkwas constructed to selected hub genes andwe found that hsa circRNA 101504 played a central role in thenetwork
2 Methods
21 Clinical Samples Six pairs of tumor and adjacent nontu-mor tissues were obtained from patients with gastric cancerwho underwent surgery at the Ruijin Hospital ShanghaiJiaotong University School of Medicine between May 2011andMay 2014 None of the patients had received neoadjuvanttherapy and the samples were pathologically confirmedpostoperatively as gastric cancer The samples were takenwithin 10min after tumor excision immediately immersedin RNAlater stabilization solution (Thermo Fisher Scien-tific Carlsbad CA USA) and then stored at minus80∘C untilbeing used in the experiments The study was performed inaccordance with the ethical standards of the Declaration ofHelsinki andwas approved by the Ethics Committee of RuijinHospital Informed consent was obtained from all patientsparticipating in the present study
22 circRNA Microarray Analysis Total RNA from eachsample was quantified using the NanoDrop ND-1000 Thesample preparation and microarray hybridization were per-formed based on Arraystarrsquos standard protocols Briefly totalRNA from each sample was amplified and transcribed intofluorescent cRNA utilizing random primer according toArraystarrsquos Super RNA Labeling protocol (Arraystar Inc)The labeled cRNAs were hybridized onto the ArraystarHuman circRNA Array (6x7K Arraystar) After havingwashed the slides the arrays were scanned by the AxonGenePix 4000B microarray scanner Scanned images werethen imported into GenePix Pro 60 software (Axon) forgrid alignment and data extraction Quantile normalizationand subsequent data processing were performed using theR software package Differentially expressed circRNAs withstatistical significance between two groups were identified
through volcano plot filtering Differentially expressed cir-cRNAs between two samples were identified through foldchange filtering Hierarchical clustering was performed toshow the distinguishable circRNAs expression pattern amongsamples
23 Annotation for circRNAmiRNA Interaction Recent evi-dences have demonstrated that circular RNAs play a crucialrole in fine tuning the level of miRNA mediated regula-tion of gene expression by sequestering the miRNAs Theirinteraction with disease associated miRNAs indicates thatcircRNAs are important for disease regulation The cir-cRNAmicroRNA interaction was predicted with Arraystarrsquoshome-made miRNA target prediction software based onTargetScan [22] and miRanda [23] and the differentiallyexpressed circRNAs within all the comparisons were anno-tated in detail with the circRNA-miRNA interaction informa-tion
24miRNADatasets andDataAnalysis TheoriginalmiRNAexpression profile of GSE23739 used in the present studywas downloaded from the National Center of BiotechnologyInformation Gene Expression Omnibus (GEO) MicroRNAexpression of twenty pairs of tissue samples collected frompatients diagnosed with gastric cancer was determined bymiRNA microarrays (platform was GPL19071) in this dataEach pair included resected primary tumor and corre-sponding healthy gastric mucosa There were no replicatesDifferentially expressed miRNAs were identified by usingGEO2RThe target genes of differentially expressed miRNAswere predicted by using TarBase v70 [24] with a predictionscore ge08 and all the miRNA-mRNA interactions wereexperimentally supported
25 Gene Function Analysis Gene ontology (GO) enrich-ment analysis of miRNA target genes was implemented withDAVID (httpdavidabccncifcrfgov) GO terms (molecu-lar function biological processes and cellular components)with 119875 value less than 005 were considered significantlyenriched by differential expressed genes Kyoto Encyclopediaof Genes and Genomes (KEGG) is a database resource forunderstanding high-level functions and effects of the biolog-ical system (httpwwwgenomejpkegg) DAVID was alsoused to test the statistical enrichment of genes or target genesof miRNA with differential expression in KEGG pathwaysThe networks of the pathways and pathway-related geneswere constructed by using Cytoscape (version 340) pluginClueGO [25] + Cluepedia [26] app
26 Construction of the circRNA-miRNA-mRNA RegulationNetwork Significantly expressed circRNAs andmiRNAs andpredicted mRNAs were superimposed onto the circRNA-miRNA-mRNA network The network was constructed byusing Cytoscape (version 340) and the network topologywas analyzed by using CentiScaPe app [27]
27 Statistical Analysis Statistical analysis was performedusing SPSS 220 (Chicago IL USA) Significant differential
BioMed Research International 3
expression levels of circRNAs or miRNAs were analyzed byStudentrsquos 119905-test and FDR filtering was used for comparativeanalysis The 119875 value le 005 and absolute fold change ge20were considered statistically significant
3 Results
31 Screening of Differentially Expressed circRNAs and miR-NAs Expression profiling data of 2070 circRNAs wereobtained by using circRNAmicroarray analysisThe circRNAexpression levels were normalized to the same order ofmagnitude prior to the statistical analysis As shown ina box plot (Figure 1(a)) the median of different sampleswas almost on the same line after normalization whichshowed a great degree of standardization The scatter plotwas used to assess the circRNA expression variation betweenthe two compared groups of samples (Figure 1(b)) Witha threshold of 119875 value le 005 and absolute value of foldchange ge20 a total of 440 differentially expressed circRNAs(176 significantly upregulated circRNAs and 264 significantlydownregulated circRNAs) were screened (Table S1) Volcanoplot was used to visualize differential expression betweentumor group and adjacent nontumor group (Figure 1(c))Hierarchical clustering was performed based on differentiallyexpressed circRNAs to hypothesize the relationships betweensamples and the result of hierarchical clustering showed adistinguishable circRNA expression profiling among samples(Figure 1(d)) The miRNA expression profile of GSE23739was analyzed by using the online tool GEO2R The box plotshowed a great degree of standardization (Figure 2) With athreshold of 119875 value le 005 and absolute value of fold changege20 a total of 111 differentially expressed miRNAs including20 upregulatedmiRNAs and 91 downregulatedmiRNAs wereidentified (Table S2)
32 Prediction of circRNA-miRNA and miRNA-mRNA Inter-action Differentially expressed circRNAs contain corre-spondingmiRNAbinding sites To facilitate the investigationthe interactions between miRNAs and circRNAs were pre-dicted by Arraystarrsquos home-made miRNA target predictionsoftwareThe circRNAs with an absolute value of fold changege50 were selected for further analysis and 260 interactionsbetween 53 circRNAs and 187 miRNAs were screened 23miRNAs were selected after differentially expressed miRNAsand circRNA-related miRNAs were intersected (Figure 3)The target genes of the 23 miRNAs were predicted by usingTarBase v70 and 206 interactions between the 23 miRNAsand 150 mRNAs were obtained
33 e GO and KEGG Enrichment Analysis of the TargetGenes GO and KEGG enrichment analysis were performedfor the selected 150 mRNAs to investigate the biologicalfunction of the circRNAs In GO analysis all the results wereranked by enrichment score (minus log(119875 value)) and top 10 ofevery category were displayed in Figure 4 In the biologi-cal process analysis anteriorposterior pattern specificationliver development and transcription and DNA-templatedwere the top 3 enriched terms In the cellular componentanalysis cytoplasmic stress granule cytosol andnucleoplasm
were the top 3 enriched terms In themolecular function anal-ysis protein binding protein kinase binding and sequence-specific DNA binding were the top 3 enriched terms Resultsof KEGG pathway analysis were also ranked by enrichmentscore and the top 10 pathways associated with the mRNAswere listed in Figure 5(a) The network composed of themost enriched pathways and their related genes (Figure 5(b))showed that PARD6B GSK3B CCND2 CCNE1 PPP2CAand CDC27 were cross-talk genes associated with at least twopathways
34 Construction of the circRNA-miRNA-mRNA Regula-tion Network A circRNA-miRNA-mRNA network was con-structed to reveal the interactions in circRNA miRNA andmRNA As shown in Figure 6 hsa-miR-27a-3p had the mostdegrees and has circRNA 101504 had the most interactionswith miRNAs indicating that they were hub genes in theregulation network Dramatically hsa-miR-93-5p and hsa-miR-20b-5p and hsa-miR-454-3p and hsa-miR-301a-3p werecoupled miRNAs which had almost the same target genesThese coupled miRNAs might coregulate the target genesin the network In graph theory betweenness centrality isa measure of centrality in a graph based on shortest pathsand devised as a general measure of centrality A node withhigher betweenness centrality would have more control overthe network becausemore informationwill pass through thatnodeThe DEGs involved in the PPI network (betweenness gt4000) were listed in Table 1
4 Discussion
Gastric cancer is one of the deadliest solid tumors character-ized by complex molecular and cellular heterogeneity Overthe past few decades great efforts have been made to providenovel insights into the molecular mechanisms underlyinggastric cancer but the focus has been on protein-coding genesor miRNAs [28 29] Recently circRNAs has been widelyreported to participate in a wide range of biological processesand their dysregulated expression is associated with manycomplicated human disease phenotypes including cancers[30 31]
In this study microarray analysis was performed toobtain the expression profiles of circRNAs in gastric cancersamples and nonmalignant pancreas samplesThe expressionprofiles of miRNAs were obtained from GEO databases andanalyzed by using GEO2R With a threshold of 119875 value lt005 and absolute fold change ge20 dysregulated circRNAsand miRNAs were identified separately After the circRNA-related miRNAs dysregulated miRNAs were intersected 23miRNAs were selected for further study Gene functionanalysis including GO analysis and KEGG pathway analysiswas conducted for the targetmRNAs of the selectedmiRNAsThe results of KEGG pathway analysis indicated that p53signaling pathway and hippo signaling pathway were signifi-cantly enriched P53 is a well-known tumor suppressor geneand the p53 mutations have been reported in many cancers[32 33] In gastric cancer He et al [34] found that Fra-1 wasupregulated in gastric cancer tissues and played its functionby affecting the PI3KAkt and p53 signaling pathway Hippo
4 BioMed Research International
Samples
16
14
12
10
8
6
4
2
Nor
mal
ized
inte
nsity
val
ues
Nor
mal1
Nor
mal2
Nor
mal3
Nor
mal4
Nor
mal5
Nor
mal6
Tum
or1
Tum
or2
Tum
or3
Tum
or4
Tum
or5
Tum
or6
(a)
16
14
12
10
8
6
4
2161412108642
Gro
up-tu
mor
(nor
mal
ized
)
Group-normal (normalized)
(b)
0
2
4
6
8
10
12
minuslog10(P
val
ue)
minus5 0 5
log 2(fold change)Tumor versus normal
(c)
Tum
or1
Tum
or2
Tum
or3
Tum
or5
Tum
or4
Tum
or6
00 75 150
Nor
mal1
Nor
mal2
Nor
mal3
Nor
mal5
Nor
mal4
Nor
mal6
(d)
Figure 1 Differentially expressed circRNAs in tumor tissues and adjacent nontumor tissues from gastric cancer patients The box plot showsthe variations in circRNA expression (a) The scatter plot (b) and the volcano plot (c) illustrate the distributions of the data in the circRNAprofilesThe result from hierarchical clustering shows a distinguishable circRNA expression profiling among samplesThe heatmap shows thedifferentially expressed circRNAs in tumor and adjacent nontumor tissues (d) Each group consists of six samples Gene expression profilesare shown in rows ldquoRedrdquo indicates high relative expression and ldquobluerdquo indicates low relative expression
BioMed Research International 5
GSM
2452859
GSM
2452861
GSM
2452862
GSM
2452864
GSM
2452866
GSM
2452868
GSM
2452870
GSM
2452873
GSM
2452874
GSM
2452876
GSM
2452879
GSM
2452880
GSM
2452882
GSM
2452885
GSM
2452886
GSM
2452889
GSM
2452890
GSM
2452893
GSM
2452894
GSM
2452897
GSM
2452858
GSM
2452860
GSM
2452863
GSM
2452865
GSM
2452867
GSM
2452869
GSM
2452871
GSM
2452872
GSM
2452875
GSM
2452877
GSM
2452878
GSM
2452881
GSM
2452883
GSM
2452884
GSM
2452887
GSM
2452888
GSM
2452891
GSM
2452892
GSM
2452895
GSM
2452896
NormalCancer
3
2
1
0
minus1
minus2
minus3
Figure 2 Differentially expressed miRNAs in tumor tissues and adjacent normal tissues from gastric cancer patientsThe box plot shows thevariations in miRNA expression Each group consists of twenty samples
Differentially expressed miRNAs circRNA-related miRNAs
Selected miRNAs
88(32)
23(84)
164(596)
Figure 3 Based on differentially expressed miRNAs and circRNA-related miRNAs the overlapped 23 miRNAs were selected usingVenn graphing
signaling pathway is a newly discovered and conservedsignaling cascade first identified in drosophila [35] Hipposignal pathway regulates organ size control by governing cellproliferation and apoptosis and is reported to be a tumor-suppressive signal pathway As shown in Figure 5(b) CCND2is an important cross-talk gene associated with cell cycle p53signaling pathway and hippo signal pathway CCND2 alsohas a high betweenness centrality in the PPI network indicat-ing that CCND2might be a bridge of a lot of interactions Forexample CCND2 is a bridge of the target genes of hsa-miR-15a-5p and hsa-miR-93-5p Zhang et al [36] have reported
thatmiR-206 could inhibit gastric cancer proliferation in partby repressing CCND2 Meanwhile another study showedthat dysregulation of miR-206-CCND2 axis might contributeto the aggressive progression and poor prognosis of humangastric cancer in clinical settings Combined detection oftheir expressionmight be particularly helpful for surveillanceof disease progression and treatment stratification [37] How-ever the relationship between circRNA and CCND2 is stillunknown In the circRNA-miRNA-mRNA regulation net-work (Figure 6) we revealed that CCND2might be regulatedby hsa circRNA 105039 and hsa cirRNA 104682 throughhsa-miR-15a-5p and hsa circRNA 105039 separately We alsofound that hsa circRNA 101504 played a central role in theregulation network As circRNAs can serve as a competitiveendogenous RNA (ceRNA) to spongemiRNAs to regulate thetarget mRNAs [38 39] upregulation of hsa circRNA 101504might affect several mRNAs by downregulating hsa-miR-454-3p and hsa-miR-301a-3p In chondrosarcoma increasinghsa-miR-454-3p can downregulate Stat3 and Atg12 to inhibitchondrosarcoma growth [40] But in human glioma hsa-miR-454-3p has the opposite effect that the prognosis ofglioma with high hsa-miR-454-3p expression is significantlyworse compared with that of glioma with low hsa-miR-454-3p expression [41] Therefore more studies of hsa-miR-454-3p involved in gastric cancer are needed
5 Conclusion
In conclusion we have screened several dysregulated cir-cRNAs through microarray analysis and annotated their
6 BioMed Research International
0 1 2 3 4 5 6Anteriorposterior pattern specification
Liver developmentTranscription DNA-templated
Positive regulation of axon extensionNegative regulation of transcription from RNA polymerase II promoter
Regulation of cell adhesionMuscle cell differentiation
G1S transition of mitotic cell cycleRegulation of mRNA stability
Negative regulation of myeloid cell differentiationCytoplasmic stress granule
CytosolNucleoplasm
NucleusSpindle poleCentrosome
Cytoplasmic mRNA processing bodyMembrane
Microtubule associated complexCytoplasm
Protein bindingProtein kinase binding
Sequence-specific DNA bindingPoly(A) RNA binding
Transcriptional repressor activity RNA polymerase II core promoter proximal region sequence-specific bindingRNA binding
Kinase activityProtein phosphatase binding
Nuclear localization sequence bindingPhosphoprotein binding
minuslog(P value)
Figure 4The top 10 enrichment scores in gene ontology (GO) enrichment analysis on target genes of selectedmiRNAs Green bars representcell component terms Blue bars represent molecular function terms
0 05 1 15 2 25 3 35 4 45
Cell cycle
Oocyte meiosis
p53 signaling pathway
Axon guidance
Measles
MicroRNAs in cancer
Hepatitis B
Prostate cancer
Hippo signaling pathway
Neurotrophin signaling pathway
minuslog(P value)
(a)
PPP2CA
CCND2
CALM3
Oocyte meiosise
ZMAT3
ITPR3
p53 signaling pathway
CDC27
CDC25B
SESN3
WEE1
E2F3
CCNE1
CDKN1B
Cell cyclecyl
SEMA4C
Axon guidancei
LATS2
EPHA7
GSK3BPATJPARD6B
SLIT2
ROCK2
Hippo signaling nalipathway
(b)
Figure 5 The KEGG pathway enrichment analysis on target genes of the selected miRNAs (a) The top 10 enrichment scores in the KEGGpathway analysis of the target genes are shown (b) The network composed of the most enriched pathways and their related genes is shown
function in gastric cancer by bioinformatics analysis We willgather more clinical samples and validate our findings infuture work
Conflicts of Interest
The authors declare that there are no conflicts of interest
Authorsrsquo Contributions
Wei Gu and Ying Sun are equal contributors to this work
Acknowledgments
The authors thank CloudSeq Inc for the bioinformatic sup-port
BioMed Research International 7
Table 1 The list of differentially expressed genes involved in the PPI network (betweenness gt 4000)
Gene name Betweenness Degree Stress Closenesshsa-miR-27a-3p 1319550 24 46060 00015hsa-miR-15a-5p 1203950 19 30488 00015NUFIP2 725859 2 1440 00015hsa-miR-148a-3p 701686 18 67106 00013hsa-miR-17-5p 634249 13 156 00014BTG2 611600 2 160 00013hsa-miR-21-5p 606000 7 42 00011DCP2 560967 2 5090 00014ARAP2 538724 4 4698 00013hsa-miR-301a-3p 516847 22 52418 00013hsa circRNA 104682 438000 2 16344 00013YOD1 438000 2 160 00010hsa-miR-196a-5p 429800 15 14042 00009CCND2 428022 2 1440 00014hsa-miR-652-5p 416600 4 7722 00011
hsa_circRNA_104575
hsa-miR-145-5p
CCNJ
hsa-miR-181b-5p
BIRC6
ANGPT2
hsa_circRNA_101222
PKD2
DYNC1LI2
SQSTM1
hsa_circRNA_104697hsa-miR-28-5p
FOXJ3
hsa-miR-193a-3p
PUM1
CDKN1B
hsa_circRNA_104168 AGPAT3
PTPRG
HOXC8
hsa-miR-196a-5p
HAND1
PSMD11
hsa_circRNA_105049
IGF2BP1
PRTG
HOXA5
KHSRP
GSK3BHOXB8
MSI2
ACLY
SLC9A6
GATAD2B
HOXA9
hsa_circRNA_103552
hsa-miR-21-5p
YOD1
BTG2
CCDC126EPHA7GATA6
HOXB6
KLHL15 FRS2 PARD6B
ADNP
CUL5
HBP1
hsa_circRNA_104634
hsa-miR-145-3p
CDC25Bhsa_circRNA_100383
KIF1BRAB14
QKI
MTPN
hsa_circRNA_103349
hsa-miR-214-3phsa_circRNA_102064
hsa_circRNA_101858
CNIH1
GALNT7
hsa_circRNA_101017
ASB1
ITPR3
BHLHE41
ADAMTS5
hsa-miR-148a-3p
NPTX1 KIAA0226
NRARP
SPIRE1
ARL6IP1 FAM178A
hsa_circRNA_101504
PGM2L1
HPRT1
hsa-miR-301a-3p
B4GALT5
CNOT4
MLLT10
PPP6R1
SESTD1ATP11A
C7orf60
hsa-miR-17-5p
ROCK2
hsa-miR-93-5p
CAMTA1
EFCAB14
hsa_circRNA_104651
RGMB
ZNF148
PSAP
RRAGDPTPN4
ZMAT3hsa_circRNA_103840
TSHZ1
STEAP4ANKRD52
TBCEL
NPAT
ARAP2
BRWD1
hsa-miR-454-3p
SMARCD2
LDLR
PPP2CAhsa-miR-652-5p
RC3H2
hsa_circRNA_104682
MAP1B
hsa_circRNA_100583
LATS2
CEP85L
hsa-miR-135b-5p
SLC6A5
CENPB
MAN2A1
ZBTB18 ZNF800
LONP2LONRF2
ABL2 PEG10
PRDM4
SLIT2
DLL1
CBX4
hsa-miR-15a-5pTMEM245
CCNE1
DDX3XWEE1
CHAC1
ARL2
PAFAH1B1RB1CC1
ATP13A3
DCP2
NABP1
SPTY2D1
hsa-miR-27a-3p
SESN3
CDK16
RORA
JARID2
CHAMP1
NUFIP2
FAM118A
MFHAS1
SEMA4C
KPNA6
hsa_circRNA_104533
hsa-miR-125b-5pZC3H7B
hsa_circRNA_100319
KPNB1
hsa_circRNA_102700
TNPO1
hsa-miR-27b-3p
CALM3
RASL11B
hsa-miR-20b-5p
hsa_circRNA_105039
PFN2
TBC1D9TMEM167A
hsa-miR-18b-5p
hsa_circRNA_102062
INADL
TAOK1
GIGYF1
CAMK2N1
CADhsa-miR-18a-5p
RNF187
E2F3
CACUL1
CDC27ZNF367
SPRTN
CCND2
ARNTL2STAT3
MAP3K9
SUV420H1
ANKRD13C
PRR15
hsa-miR-660-3p
APH1A
hsa_circRNA_104374FAM98A
hsa_circRNA_100013
NOP9
mRNAmiRNAcircRNA
minus85 85
Fold change
0
Figure 6 The visualization of the circRNA-miRNA-mRNA regulation network The circular blue nodes represent mRNAs the diamondnodes represent the miRNAs and round rectangle nodes represent the circRNAs ldquoRedrdquo indicates high relative expression and ldquogreenrdquoindicates low relative expression
8 BioMed Research International
Supplementary Materials
Table S1 differentially expressed circRNAs between tumorandnormal tissues Table S2 differentially expressedmiRNAsbetween tumor and normal tissues (Supplementary Materi-als)
References
[1] L A Torre F Bray R L Siegel J Ferlay and J Lortet-Tieu-lent ldquoGlobal cancer statistics 2012rdquo CA A Cancer Journal forClinicians vol 65 no 2 pp 87ndash108 2015
[2] J Park do N J Thomas C Yoon and S S Yoon ldquoVascularendothelial growth factor a inhibition in gastric cancerrdquoGastricCancer vol 18 no 1 pp 33ndash42 2015
[3] P Karimi F Islami S Anandasabapathy N D Freedman andF Kamangar ldquoGastric cancer descriptive epidemiology riskfactors screening and preventionrdquo Cancer Epidemiology Bio-markers amp Prevention vol 23 no 5 pp 700ndash713 2014
[4] G Spolverato A Ejaz Y Kim et al ldquoRates and patterns of re-currence after curative intent resection for gastric cancerA United States multi-institutional analysisrdquo Journal of theAmerican College of Surgeons vol 219 no 4 pp 664ndash675 2014
[5] X Qi Y Liu W Wang et al ldquoManagement of advanced gastriccancer An overview of major findings from meta-analysisrdquoOncotarget vol 7 no 47 pp 78180ndash78205 2016
[6] J E Wilusz and P A Sharp ldquoA circuitous route to noncodingRNArdquo Science vol 340 no 6131 pp 440-441 2013
[7] W R Jeck J A Sorrentino K Wang et al ldquoCircular RNAs areabundant conserved and associated with ALU repeatsrdquo RNAvol 19 no 2 pp 141ndash157 2013
[8] S Memczak M Jens A Elefsinioti et al ldquoCircular RNAs area large class of animal RNAs with regulatory potencyrdquo Naturevol 495 no 7441 pp 333ndash338 2013
[9] Y-G Zhang H-L Yang Y Long and W-L Li ldquoCircularRNA in blood corpuscles combined with plasma protein factorfor early prediction of pre-eclampsiardquo BJOG An InternationalJournal of Obstetrics amp Gynaecology vol 123 no 13 pp 2113ndash2118 2016
[10] Y Li Q Zheng C Bao et al ldquoCircular RNA is enriched andstable in exosomes a promising biomarker for cancer diagno-sisrdquo Cell Research vol 25 no 8 pp 981ndash984 2015
[11] J Guarnerio M Bezzi J C Jeong et al ldquoOncogenic Role ofFusion-circRNAs Derived from Cancer-Associated Chromoso-mal Translocationsrdquo Cell vol 165 pp 289ndash302 2016
[12] X Wang Y Zhang L Huang et al ldquoDecreased expressionof hsa circ 001988 in colorectal cancer and its clinical sig-nificancesrdquo International Journal of Clinical and ExperimentalPathology vol 8 no 12 pp 16020ndash16025 2015
[13] MQin G Liu X Huo et al ldquoHsa circ 0001649 a circular RNAand potential novel biomarker for hepatocellular carcinomardquoCancer Biomarkers vol 16 no 1 pp 161ndash169 2016
[14] L Xuan L Qu H Zhou et al ldquoCircular RNA a novel bio-marker for progressive laryngeal cancerrdquo American Journal ofTranslational Research vol 8 no 2 pp 932ndash939 2016
[15] X Song N Zhang P Han et al ldquoCircular RNA profile in glio-mas revealed by identification tool UROBORUSrdquoNucleic AcidsResearch vol 44 no 9 article no e87 2016
[16] H L Sanger G Klotz D Riesner H J Gross and A KKleinschmidt ldquoViroids are single stranded covalently closedcircular RNA molecules existing as highly base paired rod like
structuresrdquo Proceedings of the National Acadamy of Sciences ofthe United States of America vol 73 no 11 pp 3852ndash3856 1976
[17] WWDu L FangW Yang et al ldquoInduction of tumor apoptosisthrough a circular RNA enhancing Foxo3 activityrdquoCell DeathampDifferentiation vol 24 no 2 pp 357ndash370 2017
[18] M Armakola M J Higgins M D Figley et al ldquoInhibition ofRNA lariat debranching enzyme suppresses TDP-43 toxicity inALS disease modelsrdquo Nature Genetics vol 44 no 12 pp 1302ndash1309 2012
[19] Z Li C Huang C Bao et al ldquoExon-intron circular RNAs regu-late transcription in the nucleusrdquoNature Structural ampMolecularBiology vol 22 no 3 pp 256ndash264 2015
[20] R Ashwal-Fluss M Meyer N R Pamudurti et al ldquoCircRNAbiogenesis competes with pre-mRNA splicingrdquo Molecular Cellvol 56 no 1 pp 55ndash66 2014
[21] WW Du W Yang E Liu Z Yang P Dhaliwal and B B YangldquoFoxo3 circular RNA retards cell cycle progression via formingternary complexes with p21 and CDK2rdquoNucleic Acids Researchvol 44 no 6 pp 2846ndash2858 2016
[22] A J Enright B John U Gaul T Tuschl C Sander and D SMarks ldquoMicroRNA targets inDrosophilardquoGenomeBiology vol5 no 1 2003
[23] A E Pasquinelli ldquoMicroRNAs and their targets recognitionregulation and an emerging reciprocal relationshiprdquo NatureReviews Genetics vol 13 no 4 pp 271ndash282 2012
[24] I S Vlachos M D Paraskevopoulou D Karagkouni et al ldquoDI-ANA-TarBase v70 indexing more than half a million experi-mentally supported miRNAmRNA interactionsrdquoNucleic AcidsResearch vol 43 no 1 pp D153ndashD159 2015
[25] G Bindea B Mlecnik H Hackl et al ldquoClueGO a Cytoscapeplug-in to decipher functionally grouped gene ontology andpathway annotation networksrdquoBioinformatics vol 25 no 8 pp1091ndash1093 2009
[26] G Bindea J Galon and B Mlecnik ldquoCluePedia Cytoscapeplugin pathway insights using integrated experimental and insilico datardquo Bioinformatics vol 29 no 5 pp 661ndash663 2013
[27] G Scardoni M Petterlini and C Laudanna ldquoAnalyzing biolog-ical network parameters with CentiScaPerdquo Bioinformatics vol25 no 21 pp 2857ndash2859 2009
[28] M B Assumpcao F C Moreira I G Hamoy et al ldquoHigh-throughput miRNA sequencing reveals a field effect in gastriccancer and suggests an epigenetic network mechanismrdquo Bioin-formatics and Biology Insights vol 9 pp 111ndash117 2015
[29] O Yang J Huang and S Lin ldquoRegulatory effects of miRNA ongastric cancer cellsrdquo Oncology Letters vol 8 no 2 pp 651ndash6562014
[30] S Meng H Zhou Z Feng et al ldquoCircRNA Functions andproperties of a novel potential biomarker for cancerrdquoMolecularCancer vol 16 no 1 article no 94 2017
[31] L Chen S Zhang J Wu et al ldquoCircRNA-100290 plays a role inoral cancer by functioning as a sponge of the MIR-29 familyrdquoOncogene vol 36 no 32 pp 4551ndash4561 2017
[32] A Saxena S K Shukla K N Prasad and U C Ghoshal ldquoAnal-ysis of p53 K-ras gene mutation Helicobacter pylori infectionin patients with gastric cancer peptic ulcer disease at a tertiarycare hospital in north Indiardquo Indian Journal ofMedical Researchvol 136 pp 664ndash670 2012
[33] S Uchino M Noguchi A Ochiai T Saito M Kobayashi andS Hirohashi ldquop53 mutation in gastric cancer a genetic modelfor carcinogenesis is common to gastric and colorectal cancerrdquoInternational Journal of Cancer vol 54 no 5 pp 759ndash764 1993
BioMed Research International 9
[34] J He G Zhu L Gao et al ldquoFra-1 is upregulated in gastric cancertissues and affects the PI3KAkt and p53 signaling pathway ingastric cancerrdquo International Journal of Oncology vol 47 no 5pp 1725ndash1734 2015
[35] T Zheng JWang H Jiang and L Liu ldquoHippo signaling in ovalcells and hepatocarcinogenesisrdquo Cancer Letters vol 302 no 2pp 91ndash99 2011
[36] L Zhang X Liu H Jin et al ldquoMiR-206 inhibits gastric cancerproliferation in part by repressing CyclinD2rdquo Cancer Lettersvol 332 no 1 pp 94ndash101 2013
[37] H Shi J Han S Yue T Zhang W Zhu and D Zhang ldquoProg-nostic significance of combined microRNA-206 and CyclinD2in gastric cancer patients after curative surgery A retrospectivecohort studyrdquo Biomedicine amp Pharmacotherapy vol 71 pp 210ndash215 2015
[38] D W Thomson and M E Dinger ldquoEndogenous microRNAsponges evidence and controversyrdquo Nature Reviews Geneticsvol 17 no 5 pp 272ndash283 2016
[39] L Salmena L Poliseno Y Tay L Kats and P P Pandolfi ldquoAceRNA hypothesis the rosetta stone of a hidden RNA lan-guagerdquo Cell vol 146 no 3 pp 353ndash358 2011
[40] X Bao T Ren Y Huang et al ldquoKnockdown of long non-codingRNA HOTAIR increases miR-454-3p by targeting Stat3 andAtg12 to inhibit chondrosarcoma growthrdquoCell DeathampDiseasevol 8 no 2 pp e2605ndashe2605 2017
[41] N Shao L Wang L Xue R Wang and Q Lan ldquoPlasma miR-454-3p as a potential prognostic indicator in human gliomardquoNeurological Sciences vol 36 no 2 pp 309ndash313 2015
Stem Cells International
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Submit your manuscripts atwwwhindawicom
2 BioMed Research International
errors for several decades and their biological functions arelargely unknown With the development of RNA sequenc-ing (RNA-seq) technologies and bioinformatics circRNAshave been extensively explored in recent years and severalfunctions of circRNA have been revealed such as acting asscaffolds in the assembly of protein complexes [17] seques-tering proteins from their native subcellular localization [18]modulating the expression of parental genes [19] regulatingalternative splicing [20] and RNA-protein interactions [21]and functioning as microRNA (miRNA) sponges [8]
Our study aimed to establish the expression profile of gas-tric cancer through circRNA microarray chip detection Ourresults revealed the potential role of circRNAs in gastric can-cer We also aimed to identify the hub circRNAs involved ingastric cancer through bioinformatics analysis The miRNAexpression profiles from the National Center of Biotech-nology Information Gene Expression Omnibus (GEO) wereused to identify circRNA-related dysregulated miRNAs ingastric cancer Gene ontology (GO) enrichment analysis andpathway analysis revealed the potential biology functionof miRNA target genes Finally a circRNA-miRNA-mRNAregulation networkwas constructed to selected hub genes andwe found that hsa circRNA 101504 played a central role in thenetwork
2 Methods
21 Clinical Samples Six pairs of tumor and adjacent nontu-mor tissues were obtained from patients with gastric cancerwho underwent surgery at the Ruijin Hospital ShanghaiJiaotong University School of Medicine between May 2011andMay 2014 None of the patients had received neoadjuvanttherapy and the samples were pathologically confirmedpostoperatively as gastric cancer The samples were takenwithin 10min after tumor excision immediately immersedin RNAlater stabilization solution (Thermo Fisher Scien-tific Carlsbad CA USA) and then stored at minus80∘C untilbeing used in the experiments The study was performed inaccordance with the ethical standards of the Declaration ofHelsinki andwas approved by the Ethics Committee of RuijinHospital Informed consent was obtained from all patientsparticipating in the present study
22 circRNA Microarray Analysis Total RNA from eachsample was quantified using the NanoDrop ND-1000 Thesample preparation and microarray hybridization were per-formed based on Arraystarrsquos standard protocols Briefly totalRNA from each sample was amplified and transcribed intofluorescent cRNA utilizing random primer according toArraystarrsquos Super RNA Labeling protocol (Arraystar Inc)The labeled cRNAs were hybridized onto the ArraystarHuman circRNA Array (6x7K Arraystar) After havingwashed the slides the arrays were scanned by the AxonGenePix 4000B microarray scanner Scanned images werethen imported into GenePix Pro 60 software (Axon) forgrid alignment and data extraction Quantile normalizationand subsequent data processing were performed using theR software package Differentially expressed circRNAs withstatistical significance between two groups were identified
through volcano plot filtering Differentially expressed cir-cRNAs between two samples were identified through foldchange filtering Hierarchical clustering was performed toshow the distinguishable circRNAs expression pattern amongsamples
23 Annotation for circRNAmiRNA Interaction Recent evi-dences have demonstrated that circular RNAs play a crucialrole in fine tuning the level of miRNA mediated regula-tion of gene expression by sequestering the miRNAs Theirinteraction with disease associated miRNAs indicates thatcircRNAs are important for disease regulation The cir-cRNAmicroRNA interaction was predicted with Arraystarrsquoshome-made miRNA target prediction software based onTargetScan [22] and miRanda [23] and the differentiallyexpressed circRNAs within all the comparisons were anno-tated in detail with the circRNA-miRNA interaction informa-tion
24miRNADatasets andDataAnalysis TheoriginalmiRNAexpression profile of GSE23739 used in the present studywas downloaded from the National Center of BiotechnologyInformation Gene Expression Omnibus (GEO) MicroRNAexpression of twenty pairs of tissue samples collected frompatients diagnosed with gastric cancer was determined bymiRNA microarrays (platform was GPL19071) in this dataEach pair included resected primary tumor and corre-sponding healthy gastric mucosa There were no replicatesDifferentially expressed miRNAs were identified by usingGEO2RThe target genes of differentially expressed miRNAswere predicted by using TarBase v70 [24] with a predictionscore ge08 and all the miRNA-mRNA interactions wereexperimentally supported
25 Gene Function Analysis Gene ontology (GO) enrich-ment analysis of miRNA target genes was implemented withDAVID (httpdavidabccncifcrfgov) GO terms (molecu-lar function biological processes and cellular components)with 119875 value less than 005 were considered significantlyenriched by differential expressed genes Kyoto Encyclopediaof Genes and Genomes (KEGG) is a database resource forunderstanding high-level functions and effects of the biolog-ical system (httpwwwgenomejpkegg) DAVID was alsoused to test the statistical enrichment of genes or target genesof miRNA with differential expression in KEGG pathwaysThe networks of the pathways and pathway-related geneswere constructed by using Cytoscape (version 340) pluginClueGO [25] + Cluepedia [26] app
26 Construction of the circRNA-miRNA-mRNA RegulationNetwork Significantly expressed circRNAs andmiRNAs andpredicted mRNAs were superimposed onto the circRNA-miRNA-mRNA network The network was constructed byusing Cytoscape (version 340) and the network topologywas analyzed by using CentiScaPe app [27]
27 Statistical Analysis Statistical analysis was performedusing SPSS 220 (Chicago IL USA) Significant differential
BioMed Research International 3
expression levels of circRNAs or miRNAs were analyzed byStudentrsquos 119905-test and FDR filtering was used for comparativeanalysis The 119875 value le 005 and absolute fold change ge20were considered statistically significant
3 Results
31 Screening of Differentially Expressed circRNAs and miR-NAs Expression profiling data of 2070 circRNAs wereobtained by using circRNAmicroarray analysisThe circRNAexpression levels were normalized to the same order ofmagnitude prior to the statistical analysis As shown ina box plot (Figure 1(a)) the median of different sampleswas almost on the same line after normalization whichshowed a great degree of standardization The scatter plotwas used to assess the circRNA expression variation betweenthe two compared groups of samples (Figure 1(b)) Witha threshold of 119875 value le 005 and absolute value of foldchange ge20 a total of 440 differentially expressed circRNAs(176 significantly upregulated circRNAs and 264 significantlydownregulated circRNAs) were screened (Table S1) Volcanoplot was used to visualize differential expression betweentumor group and adjacent nontumor group (Figure 1(c))Hierarchical clustering was performed based on differentiallyexpressed circRNAs to hypothesize the relationships betweensamples and the result of hierarchical clustering showed adistinguishable circRNA expression profiling among samples(Figure 1(d)) The miRNA expression profile of GSE23739was analyzed by using the online tool GEO2R The box plotshowed a great degree of standardization (Figure 2) With athreshold of 119875 value le 005 and absolute value of fold changege20 a total of 111 differentially expressed miRNAs including20 upregulatedmiRNAs and 91 downregulatedmiRNAs wereidentified (Table S2)
32 Prediction of circRNA-miRNA and miRNA-mRNA Inter-action Differentially expressed circRNAs contain corre-spondingmiRNAbinding sites To facilitate the investigationthe interactions between miRNAs and circRNAs were pre-dicted by Arraystarrsquos home-made miRNA target predictionsoftwareThe circRNAs with an absolute value of fold changege50 were selected for further analysis and 260 interactionsbetween 53 circRNAs and 187 miRNAs were screened 23miRNAs were selected after differentially expressed miRNAsand circRNA-related miRNAs were intersected (Figure 3)The target genes of the 23 miRNAs were predicted by usingTarBase v70 and 206 interactions between the 23 miRNAsand 150 mRNAs were obtained
33 e GO and KEGG Enrichment Analysis of the TargetGenes GO and KEGG enrichment analysis were performedfor the selected 150 mRNAs to investigate the biologicalfunction of the circRNAs In GO analysis all the results wereranked by enrichment score (minus log(119875 value)) and top 10 ofevery category were displayed in Figure 4 In the biologi-cal process analysis anteriorposterior pattern specificationliver development and transcription and DNA-templatedwere the top 3 enriched terms In the cellular componentanalysis cytoplasmic stress granule cytosol andnucleoplasm
were the top 3 enriched terms In themolecular function anal-ysis protein binding protein kinase binding and sequence-specific DNA binding were the top 3 enriched terms Resultsof KEGG pathway analysis were also ranked by enrichmentscore and the top 10 pathways associated with the mRNAswere listed in Figure 5(a) The network composed of themost enriched pathways and their related genes (Figure 5(b))showed that PARD6B GSK3B CCND2 CCNE1 PPP2CAand CDC27 were cross-talk genes associated with at least twopathways
34 Construction of the circRNA-miRNA-mRNA Regula-tion Network A circRNA-miRNA-mRNA network was con-structed to reveal the interactions in circRNA miRNA andmRNA As shown in Figure 6 hsa-miR-27a-3p had the mostdegrees and has circRNA 101504 had the most interactionswith miRNAs indicating that they were hub genes in theregulation network Dramatically hsa-miR-93-5p and hsa-miR-20b-5p and hsa-miR-454-3p and hsa-miR-301a-3p werecoupled miRNAs which had almost the same target genesThese coupled miRNAs might coregulate the target genesin the network In graph theory betweenness centrality isa measure of centrality in a graph based on shortest pathsand devised as a general measure of centrality A node withhigher betweenness centrality would have more control overthe network becausemore informationwill pass through thatnodeThe DEGs involved in the PPI network (betweenness gt4000) were listed in Table 1
4 Discussion
Gastric cancer is one of the deadliest solid tumors character-ized by complex molecular and cellular heterogeneity Overthe past few decades great efforts have been made to providenovel insights into the molecular mechanisms underlyinggastric cancer but the focus has been on protein-coding genesor miRNAs [28 29] Recently circRNAs has been widelyreported to participate in a wide range of biological processesand their dysregulated expression is associated with manycomplicated human disease phenotypes including cancers[30 31]
In this study microarray analysis was performed toobtain the expression profiles of circRNAs in gastric cancersamples and nonmalignant pancreas samplesThe expressionprofiles of miRNAs were obtained from GEO databases andanalyzed by using GEO2R With a threshold of 119875 value lt005 and absolute fold change ge20 dysregulated circRNAsand miRNAs were identified separately After the circRNA-related miRNAs dysregulated miRNAs were intersected 23miRNAs were selected for further study Gene functionanalysis including GO analysis and KEGG pathway analysiswas conducted for the targetmRNAs of the selectedmiRNAsThe results of KEGG pathway analysis indicated that p53signaling pathway and hippo signaling pathway were signifi-cantly enriched P53 is a well-known tumor suppressor geneand the p53 mutations have been reported in many cancers[32 33] In gastric cancer He et al [34] found that Fra-1 wasupregulated in gastric cancer tissues and played its functionby affecting the PI3KAkt and p53 signaling pathway Hippo
4 BioMed Research International
Samples
16
14
12
10
8
6
4
2
Nor
mal
ized
inte
nsity
val
ues
Nor
mal1
Nor
mal2
Nor
mal3
Nor
mal4
Nor
mal5
Nor
mal6
Tum
or1
Tum
or2
Tum
or3
Tum
or4
Tum
or5
Tum
or6
(a)
16
14
12
10
8
6
4
2161412108642
Gro
up-tu
mor
(nor
mal
ized
)
Group-normal (normalized)
(b)
0
2
4
6
8
10
12
minuslog10(P
val
ue)
minus5 0 5
log 2(fold change)Tumor versus normal
(c)
Tum
or1
Tum
or2
Tum
or3
Tum
or5
Tum
or4
Tum
or6
00 75 150
Nor
mal1
Nor
mal2
Nor
mal3
Nor
mal5
Nor
mal4
Nor
mal6
(d)
Figure 1 Differentially expressed circRNAs in tumor tissues and adjacent nontumor tissues from gastric cancer patients The box plot showsthe variations in circRNA expression (a) The scatter plot (b) and the volcano plot (c) illustrate the distributions of the data in the circRNAprofilesThe result from hierarchical clustering shows a distinguishable circRNA expression profiling among samplesThe heatmap shows thedifferentially expressed circRNAs in tumor and adjacent nontumor tissues (d) Each group consists of six samples Gene expression profilesare shown in rows ldquoRedrdquo indicates high relative expression and ldquobluerdquo indicates low relative expression
BioMed Research International 5
GSM
2452859
GSM
2452861
GSM
2452862
GSM
2452864
GSM
2452866
GSM
2452868
GSM
2452870
GSM
2452873
GSM
2452874
GSM
2452876
GSM
2452879
GSM
2452880
GSM
2452882
GSM
2452885
GSM
2452886
GSM
2452889
GSM
2452890
GSM
2452893
GSM
2452894
GSM
2452897
GSM
2452858
GSM
2452860
GSM
2452863
GSM
2452865
GSM
2452867
GSM
2452869
GSM
2452871
GSM
2452872
GSM
2452875
GSM
2452877
GSM
2452878
GSM
2452881
GSM
2452883
GSM
2452884
GSM
2452887
GSM
2452888
GSM
2452891
GSM
2452892
GSM
2452895
GSM
2452896
NormalCancer
3
2
1
0
minus1
minus2
minus3
Figure 2 Differentially expressed miRNAs in tumor tissues and adjacent normal tissues from gastric cancer patientsThe box plot shows thevariations in miRNA expression Each group consists of twenty samples
Differentially expressed miRNAs circRNA-related miRNAs
Selected miRNAs
88(32)
23(84)
164(596)
Figure 3 Based on differentially expressed miRNAs and circRNA-related miRNAs the overlapped 23 miRNAs were selected usingVenn graphing
signaling pathway is a newly discovered and conservedsignaling cascade first identified in drosophila [35] Hipposignal pathway regulates organ size control by governing cellproliferation and apoptosis and is reported to be a tumor-suppressive signal pathway As shown in Figure 5(b) CCND2is an important cross-talk gene associated with cell cycle p53signaling pathway and hippo signal pathway CCND2 alsohas a high betweenness centrality in the PPI network indicat-ing that CCND2might be a bridge of a lot of interactions Forexample CCND2 is a bridge of the target genes of hsa-miR-15a-5p and hsa-miR-93-5p Zhang et al [36] have reported
thatmiR-206 could inhibit gastric cancer proliferation in partby repressing CCND2 Meanwhile another study showedthat dysregulation of miR-206-CCND2 axis might contributeto the aggressive progression and poor prognosis of humangastric cancer in clinical settings Combined detection oftheir expressionmight be particularly helpful for surveillanceof disease progression and treatment stratification [37] How-ever the relationship between circRNA and CCND2 is stillunknown In the circRNA-miRNA-mRNA regulation net-work (Figure 6) we revealed that CCND2might be regulatedby hsa circRNA 105039 and hsa cirRNA 104682 throughhsa-miR-15a-5p and hsa circRNA 105039 separately We alsofound that hsa circRNA 101504 played a central role in theregulation network As circRNAs can serve as a competitiveendogenous RNA (ceRNA) to spongemiRNAs to regulate thetarget mRNAs [38 39] upregulation of hsa circRNA 101504might affect several mRNAs by downregulating hsa-miR-454-3p and hsa-miR-301a-3p In chondrosarcoma increasinghsa-miR-454-3p can downregulate Stat3 and Atg12 to inhibitchondrosarcoma growth [40] But in human glioma hsa-miR-454-3p has the opposite effect that the prognosis ofglioma with high hsa-miR-454-3p expression is significantlyworse compared with that of glioma with low hsa-miR-454-3p expression [41] Therefore more studies of hsa-miR-454-3p involved in gastric cancer are needed
5 Conclusion
In conclusion we have screened several dysregulated cir-cRNAs through microarray analysis and annotated their
6 BioMed Research International
0 1 2 3 4 5 6Anteriorposterior pattern specification
Liver developmentTranscription DNA-templated
Positive regulation of axon extensionNegative regulation of transcription from RNA polymerase II promoter
Regulation of cell adhesionMuscle cell differentiation
G1S transition of mitotic cell cycleRegulation of mRNA stability
Negative regulation of myeloid cell differentiationCytoplasmic stress granule
CytosolNucleoplasm
NucleusSpindle poleCentrosome
Cytoplasmic mRNA processing bodyMembrane
Microtubule associated complexCytoplasm
Protein bindingProtein kinase binding
Sequence-specific DNA bindingPoly(A) RNA binding
Transcriptional repressor activity RNA polymerase II core promoter proximal region sequence-specific bindingRNA binding
Kinase activityProtein phosphatase binding
Nuclear localization sequence bindingPhosphoprotein binding
minuslog(P value)
Figure 4The top 10 enrichment scores in gene ontology (GO) enrichment analysis on target genes of selectedmiRNAs Green bars representcell component terms Blue bars represent molecular function terms
0 05 1 15 2 25 3 35 4 45
Cell cycle
Oocyte meiosis
p53 signaling pathway
Axon guidance
Measles
MicroRNAs in cancer
Hepatitis B
Prostate cancer
Hippo signaling pathway
Neurotrophin signaling pathway
minuslog(P value)
(a)
PPP2CA
CCND2
CALM3
Oocyte meiosise
ZMAT3
ITPR3
p53 signaling pathway
CDC27
CDC25B
SESN3
WEE1
E2F3
CCNE1
CDKN1B
Cell cyclecyl
SEMA4C
Axon guidancei
LATS2
EPHA7
GSK3BPATJPARD6B
SLIT2
ROCK2
Hippo signaling nalipathway
(b)
Figure 5 The KEGG pathway enrichment analysis on target genes of the selected miRNAs (a) The top 10 enrichment scores in the KEGGpathway analysis of the target genes are shown (b) The network composed of the most enriched pathways and their related genes is shown
function in gastric cancer by bioinformatics analysis We willgather more clinical samples and validate our findings infuture work
Conflicts of Interest
The authors declare that there are no conflicts of interest
Authorsrsquo Contributions
Wei Gu and Ying Sun are equal contributors to this work
Acknowledgments
The authors thank CloudSeq Inc for the bioinformatic sup-port
BioMed Research International 7
Table 1 The list of differentially expressed genes involved in the PPI network (betweenness gt 4000)
Gene name Betweenness Degree Stress Closenesshsa-miR-27a-3p 1319550 24 46060 00015hsa-miR-15a-5p 1203950 19 30488 00015NUFIP2 725859 2 1440 00015hsa-miR-148a-3p 701686 18 67106 00013hsa-miR-17-5p 634249 13 156 00014BTG2 611600 2 160 00013hsa-miR-21-5p 606000 7 42 00011DCP2 560967 2 5090 00014ARAP2 538724 4 4698 00013hsa-miR-301a-3p 516847 22 52418 00013hsa circRNA 104682 438000 2 16344 00013YOD1 438000 2 160 00010hsa-miR-196a-5p 429800 15 14042 00009CCND2 428022 2 1440 00014hsa-miR-652-5p 416600 4 7722 00011
hsa_circRNA_104575
hsa-miR-145-5p
CCNJ
hsa-miR-181b-5p
BIRC6
ANGPT2
hsa_circRNA_101222
PKD2
DYNC1LI2
SQSTM1
hsa_circRNA_104697hsa-miR-28-5p
FOXJ3
hsa-miR-193a-3p
PUM1
CDKN1B
hsa_circRNA_104168 AGPAT3
PTPRG
HOXC8
hsa-miR-196a-5p
HAND1
PSMD11
hsa_circRNA_105049
IGF2BP1
PRTG
HOXA5
KHSRP
GSK3BHOXB8
MSI2
ACLY
SLC9A6
GATAD2B
HOXA9
hsa_circRNA_103552
hsa-miR-21-5p
YOD1
BTG2
CCDC126EPHA7GATA6
HOXB6
KLHL15 FRS2 PARD6B
ADNP
CUL5
HBP1
hsa_circRNA_104634
hsa-miR-145-3p
CDC25Bhsa_circRNA_100383
KIF1BRAB14
QKI
MTPN
hsa_circRNA_103349
hsa-miR-214-3phsa_circRNA_102064
hsa_circRNA_101858
CNIH1
GALNT7
hsa_circRNA_101017
ASB1
ITPR3
BHLHE41
ADAMTS5
hsa-miR-148a-3p
NPTX1 KIAA0226
NRARP
SPIRE1
ARL6IP1 FAM178A
hsa_circRNA_101504
PGM2L1
HPRT1
hsa-miR-301a-3p
B4GALT5
CNOT4
MLLT10
PPP6R1
SESTD1ATP11A
C7orf60
hsa-miR-17-5p
ROCK2
hsa-miR-93-5p
CAMTA1
EFCAB14
hsa_circRNA_104651
RGMB
ZNF148
PSAP
RRAGDPTPN4
ZMAT3hsa_circRNA_103840
TSHZ1
STEAP4ANKRD52
TBCEL
NPAT
ARAP2
BRWD1
hsa-miR-454-3p
SMARCD2
LDLR
PPP2CAhsa-miR-652-5p
RC3H2
hsa_circRNA_104682
MAP1B
hsa_circRNA_100583
LATS2
CEP85L
hsa-miR-135b-5p
SLC6A5
CENPB
MAN2A1
ZBTB18 ZNF800
LONP2LONRF2
ABL2 PEG10
PRDM4
SLIT2
DLL1
CBX4
hsa-miR-15a-5pTMEM245
CCNE1
DDX3XWEE1
CHAC1
ARL2
PAFAH1B1RB1CC1
ATP13A3
DCP2
NABP1
SPTY2D1
hsa-miR-27a-3p
SESN3
CDK16
RORA
JARID2
CHAMP1
NUFIP2
FAM118A
MFHAS1
SEMA4C
KPNA6
hsa_circRNA_104533
hsa-miR-125b-5pZC3H7B
hsa_circRNA_100319
KPNB1
hsa_circRNA_102700
TNPO1
hsa-miR-27b-3p
CALM3
RASL11B
hsa-miR-20b-5p
hsa_circRNA_105039
PFN2
TBC1D9TMEM167A
hsa-miR-18b-5p
hsa_circRNA_102062
INADL
TAOK1
GIGYF1
CAMK2N1
CADhsa-miR-18a-5p
RNF187
E2F3
CACUL1
CDC27ZNF367
SPRTN
CCND2
ARNTL2STAT3
MAP3K9
SUV420H1
ANKRD13C
PRR15
hsa-miR-660-3p
APH1A
hsa_circRNA_104374FAM98A
hsa_circRNA_100013
NOP9
mRNAmiRNAcircRNA
minus85 85
Fold change
0
Figure 6 The visualization of the circRNA-miRNA-mRNA regulation network The circular blue nodes represent mRNAs the diamondnodes represent the miRNAs and round rectangle nodes represent the circRNAs ldquoRedrdquo indicates high relative expression and ldquogreenrdquoindicates low relative expression
8 BioMed Research International
Supplementary Materials
Table S1 differentially expressed circRNAs between tumorandnormal tissues Table S2 differentially expressedmiRNAsbetween tumor and normal tissues (Supplementary Materi-als)
References
[1] L A Torre F Bray R L Siegel J Ferlay and J Lortet-Tieu-lent ldquoGlobal cancer statistics 2012rdquo CA A Cancer Journal forClinicians vol 65 no 2 pp 87ndash108 2015
[2] J Park do N J Thomas C Yoon and S S Yoon ldquoVascularendothelial growth factor a inhibition in gastric cancerrdquoGastricCancer vol 18 no 1 pp 33ndash42 2015
[3] P Karimi F Islami S Anandasabapathy N D Freedman andF Kamangar ldquoGastric cancer descriptive epidemiology riskfactors screening and preventionrdquo Cancer Epidemiology Bio-markers amp Prevention vol 23 no 5 pp 700ndash713 2014
[4] G Spolverato A Ejaz Y Kim et al ldquoRates and patterns of re-currence after curative intent resection for gastric cancerA United States multi-institutional analysisrdquo Journal of theAmerican College of Surgeons vol 219 no 4 pp 664ndash675 2014
[5] X Qi Y Liu W Wang et al ldquoManagement of advanced gastriccancer An overview of major findings from meta-analysisrdquoOncotarget vol 7 no 47 pp 78180ndash78205 2016
[6] J E Wilusz and P A Sharp ldquoA circuitous route to noncodingRNArdquo Science vol 340 no 6131 pp 440-441 2013
[7] W R Jeck J A Sorrentino K Wang et al ldquoCircular RNAs areabundant conserved and associated with ALU repeatsrdquo RNAvol 19 no 2 pp 141ndash157 2013
[8] S Memczak M Jens A Elefsinioti et al ldquoCircular RNAs area large class of animal RNAs with regulatory potencyrdquo Naturevol 495 no 7441 pp 333ndash338 2013
[9] Y-G Zhang H-L Yang Y Long and W-L Li ldquoCircularRNA in blood corpuscles combined with plasma protein factorfor early prediction of pre-eclampsiardquo BJOG An InternationalJournal of Obstetrics amp Gynaecology vol 123 no 13 pp 2113ndash2118 2016
[10] Y Li Q Zheng C Bao et al ldquoCircular RNA is enriched andstable in exosomes a promising biomarker for cancer diagno-sisrdquo Cell Research vol 25 no 8 pp 981ndash984 2015
[11] J Guarnerio M Bezzi J C Jeong et al ldquoOncogenic Role ofFusion-circRNAs Derived from Cancer-Associated Chromoso-mal Translocationsrdquo Cell vol 165 pp 289ndash302 2016
[12] X Wang Y Zhang L Huang et al ldquoDecreased expressionof hsa circ 001988 in colorectal cancer and its clinical sig-nificancesrdquo International Journal of Clinical and ExperimentalPathology vol 8 no 12 pp 16020ndash16025 2015
[13] MQin G Liu X Huo et al ldquoHsa circ 0001649 a circular RNAand potential novel biomarker for hepatocellular carcinomardquoCancer Biomarkers vol 16 no 1 pp 161ndash169 2016
[14] L Xuan L Qu H Zhou et al ldquoCircular RNA a novel bio-marker for progressive laryngeal cancerrdquo American Journal ofTranslational Research vol 8 no 2 pp 932ndash939 2016
[15] X Song N Zhang P Han et al ldquoCircular RNA profile in glio-mas revealed by identification tool UROBORUSrdquoNucleic AcidsResearch vol 44 no 9 article no e87 2016
[16] H L Sanger G Klotz D Riesner H J Gross and A KKleinschmidt ldquoViroids are single stranded covalently closedcircular RNA molecules existing as highly base paired rod like
structuresrdquo Proceedings of the National Acadamy of Sciences ofthe United States of America vol 73 no 11 pp 3852ndash3856 1976
[17] WWDu L FangW Yang et al ldquoInduction of tumor apoptosisthrough a circular RNA enhancing Foxo3 activityrdquoCell DeathampDifferentiation vol 24 no 2 pp 357ndash370 2017
[18] M Armakola M J Higgins M D Figley et al ldquoInhibition ofRNA lariat debranching enzyme suppresses TDP-43 toxicity inALS disease modelsrdquo Nature Genetics vol 44 no 12 pp 1302ndash1309 2012
[19] Z Li C Huang C Bao et al ldquoExon-intron circular RNAs regu-late transcription in the nucleusrdquoNature Structural ampMolecularBiology vol 22 no 3 pp 256ndash264 2015
[20] R Ashwal-Fluss M Meyer N R Pamudurti et al ldquoCircRNAbiogenesis competes with pre-mRNA splicingrdquo Molecular Cellvol 56 no 1 pp 55ndash66 2014
[21] WW Du W Yang E Liu Z Yang P Dhaliwal and B B YangldquoFoxo3 circular RNA retards cell cycle progression via formingternary complexes with p21 and CDK2rdquoNucleic Acids Researchvol 44 no 6 pp 2846ndash2858 2016
[22] A J Enright B John U Gaul T Tuschl C Sander and D SMarks ldquoMicroRNA targets inDrosophilardquoGenomeBiology vol5 no 1 2003
[23] A E Pasquinelli ldquoMicroRNAs and their targets recognitionregulation and an emerging reciprocal relationshiprdquo NatureReviews Genetics vol 13 no 4 pp 271ndash282 2012
[24] I S Vlachos M D Paraskevopoulou D Karagkouni et al ldquoDI-ANA-TarBase v70 indexing more than half a million experi-mentally supported miRNAmRNA interactionsrdquoNucleic AcidsResearch vol 43 no 1 pp D153ndashD159 2015
[25] G Bindea B Mlecnik H Hackl et al ldquoClueGO a Cytoscapeplug-in to decipher functionally grouped gene ontology andpathway annotation networksrdquoBioinformatics vol 25 no 8 pp1091ndash1093 2009
[26] G Bindea J Galon and B Mlecnik ldquoCluePedia Cytoscapeplugin pathway insights using integrated experimental and insilico datardquo Bioinformatics vol 29 no 5 pp 661ndash663 2013
[27] G Scardoni M Petterlini and C Laudanna ldquoAnalyzing biolog-ical network parameters with CentiScaPerdquo Bioinformatics vol25 no 21 pp 2857ndash2859 2009
[28] M B Assumpcao F C Moreira I G Hamoy et al ldquoHigh-throughput miRNA sequencing reveals a field effect in gastriccancer and suggests an epigenetic network mechanismrdquo Bioin-formatics and Biology Insights vol 9 pp 111ndash117 2015
[29] O Yang J Huang and S Lin ldquoRegulatory effects of miRNA ongastric cancer cellsrdquo Oncology Letters vol 8 no 2 pp 651ndash6562014
[30] S Meng H Zhou Z Feng et al ldquoCircRNA Functions andproperties of a novel potential biomarker for cancerrdquoMolecularCancer vol 16 no 1 article no 94 2017
[31] L Chen S Zhang J Wu et al ldquoCircRNA-100290 plays a role inoral cancer by functioning as a sponge of the MIR-29 familyrdquoOncogene vol 36 no 32 pp 4551ndash4561 2017
[32] A Saxena S K Shukla K N Prasad and U C Ghoshal ldquoAnal-ysis of p53 K-ras gene mutation Helicobacter pylori infectionin patients with gastric cancer peptic ulcer disease at a tertiarycare hospital in north Indiardquo Indian Journal ofMedical Researchvol 136 pp 664ndash670 2012
[33] S Uchino M Noguchi A Ochiai T Saito M Kobayashi andS Hirohashi ldquop53 mutation in gastric cancer a genetic modelfor carcinogenesis is common to gastric and colorectal cancerrdquoInternational Journal of Cancer vol 54 no 5 pp 759ndash764 1993
BioMed Research International 9
[34] J He G Zhu L Gao et al ldquoFra-1 is upregulated in gastric cancertissues and affects the PI3KAkt and p53 signaling pathway ingastric cancerrdquo International Journal of Oncology vol 47 no 5pp 1725ndash1734 2015
[35] T Zheng JWang H Jiang and L Liu ldquoHippo signaling in ovalcells and hepatocarcinogenesisrdquo Cancer Letters vol 302 no 2pp 91ndash99 2011
[36] L Zhang X Liu H Jin et al ldquoMiR-206 inhibits gastric cancerproliferation in part by repressing CyclinD2rdquo Cancer Lettersvol 332 no 1 pp 94ndash101 2013
[37] H Shi J Han S Yue T Zhang W Zhu and D Zhang ldquoProg-nostic significance of combined microRNA-206 and CyclinD2in gastric cancer patients after curative surgery A retrospectivecohort studyrdquo Biomedicine amp Pharmacotherapy vol 71 pp 210ndash215 2015
[38] D W Thomson and M E Dinger ldquoEndogenous microRNAsponges evidence and controversyrdquo Nature Reviews Geneticsvol 17 no 5 pp 272ndash283 2016
[39] L Salmena L Poliseno Y Tay L Kats and P P Pandolfi ldquoAceRNA hypothesis the rosetta stone of a hidden RNA lan-guagerdquo Cell vol 146 no 3 pp 353ndash358 2011
[40] X Bao T Ren Y Huang et al ldquoKnockdown of long non-codingRNA HOTAIR increases miR-454-3p by targeting Stat3 andAtg12 to inhibit chondrosarcoma growthrdquoCell DeathampDiseasevol 8 no 2 pp e2605ndashe2605 2017
[41] N Shao L Wang L Xue R Wang and Q Lan ldquoPlasma miR-454-3p as a potential prognostic indicator in human gliomardquoNeurological Sciences vol 36 no 2 pp 309ndash313 2015
Stem Cells International
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
MEDIATORSINFLAMMATION
of
EndocrinologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Disease Markers
Hindawiwwwhindawicom Volume 2018
BioMed Research International
OncologyJournal of
Hindawiwwwhindawicom Volume 2013
Hindawiwwwhindawicom Volume 2018
Oxidative Medicine and Cellular Longevity
Hindawiwwwhindawicom Volume 2018
PPAR Research
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018
Immunology ResearchHindawiwwwhindawicom Volume 2018
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Evidence-Based Complementary andAlternative Medicine
Volume 2018Hindawiwwwhindawicom
Submit your manuscripts atwwwhindawicom
BioMed Research International 3
expression levels of circRNAs or miRNAs were analyzed byStudentrsquos 119905-test and FDR filtering was used for comparativeanalysis The 119875 value le 005 and absolute fold change ge20were considered statistically significant
3 Results
31 Screening of Differentially Expressed circRNAs and miR-NAs Expression profiling data of 2070 circRNAs wereobtained by using circRNAmicroarray analysisThe circRNAexpression levels were normalized to the same order ofmagnitude prior to the statistical analysis As shown ina box plot (Figure 1(a)) the median of different sampleswas almost on the same line after normalization whichshowed a great degree of standardization The scatter plotwas used to assess the circRNA expression variation betweenthe two compared groups of samples (Figure 1(b)) Witha threshold of 119875 value le 005 and absolute value of foldchange ge20 a total of 440 differentially expressed circRNAs(176 significantly upregulated circRNAs and 264 significantlydownregulated circRNAs) were screened (Table S1) Volcanoplot was used to visualize differential expression betweentumor group and adjacent nontumor group (Figure 1(c))Hierarchical clustering was performed based on differentiallyexpressed circRNAs to hypothesize the relationships betweensamples and the result of hierarchical clustering showed adistinguishable circRNA expression profiling among samples(Figure 1(d)) The miRNA expression profile of GSE23739was analyzed by using the online tool GEO2R The box plotshowed a great degree of standardization (Figure 2) With athreshold of 119875 value le 005 and absolute value of fold changege20 a total of 111 differentially expressed miRNAs including20 upregulatedmiRNAs and 91 downregulatedmiRNAs wereidentified (Table S2)
32 Prediction of circRNA-miRNA and miRNA-mRNA Inter-action Differentially expressed circRNAs contain corre-spondingmiRNAbinding sites To facilitate the investigationthe interactions between miRNAs and circRNAs were pre-dicted by Arraystarrsquos home-made miRNA target predictionsoftwareThe circRNAs with an absolute value of fold changege50 were selected for further analysis and 260 interactionsbetween 53 circRNAs and 187 miRNAs were screened 23miRNAs were selected after differentially expressed miRNAsand circRNA-related miRNAs were intersected (Figure 3)The target genes of the 23 miRNAs were predicted by usingTarBase v70 and 206 interactions between the 23 miRNAsand 150 mRNAs were obtained
33 e GO and KEGG Enrichment Analysis of the TargetGenes GO and KEGG enrichment analysis were performedfor the selected 150 mRNAs to investigate the biologicalfunction of the circRNAs In GO analysis all the results wereranked by enrichment score (minus log(119875 value)) and top 10 ofevery category were displayed in Figure 4 In the biologi-cal process analysis anteriorposterior pattern specificationliver development and transcription and DNA-templatedwere the top 3 enriched terms In the cellular componentanalysis cytoplasmic stress granule cytosol andnucleoplasm
were the top 3 enriched terms In themolecular function anal-ysis protein binding protein kinase binding and sequence-specific DNA binding were the top 3 enriched terms Resultsof KEGG pathway analysis were also ranked by enrichmentscore and the top 10 pathways associated with the mRNAswere listed in Figure 5(a) The network composed of themost enriched pathways and their related genes (Figure 5(b))showed that PARD6B GSK3B CCND2 CCNE1 PPP2CAand CDC27 were cross-talk genes associated with at least twopathways
34 Construction of the circRNA-miRNA-mRNA Regula-tion Network A circRNA-miRNA-mRNA network was con-structed to reveal the interactions in circRNA miRNA andmRNA As shown in Figure 6 hsa-miR-27a-3p had the mostdegrees and has circRNA 101504 had the most interactionswith miRNAs indicating that they were hub genes in theregulation network Dramatically hsa-miR-93-5p and hsa-miR-20b-5p and hsa-miR-454-3p and hsa-miR-301a-3p werecoupled miRNAs which had almost the same target genesThese coupled miRNAs might coregulate the target genesin the network In graph theory betweenness centrality isa measure of centrality in a graph based on shortest pathsand devised as a general measure of centrality A node withhigher betweenness centrality would have more control overthe network becausemore informationwill pass through thatnodeThe DEGs involved in the PPI network (betweenness gt4000) were listed in Table 1
4 Discussion
Gastric cancer is one of the deadliest solid tumors character-ized by complex molecular and cellular heterogeneity Overthe past few decades great efforts have been made to providenovel insights into the molecular mechanisms underlyinggastric cancer but the focus has been on protein-coding genesor miRNAs [28 29] Recently circRNAs has been widelyreported to participate in a wide range of biological processesand their dysregulated expression is associated with manycomplicated human disease phenotypes including cancers[30 31]
In this study microarray analysis was performed toobtain the expression profiles of circRNAs in gastric cancersamples and nonmalignant pancreas samplesThe expressionprofiles of miRNAs were obtained from GEO databases andanalyzed by using GEO2R With a threshold of 119875 value lt005 and absolute fold change ge20 dysregulated circRNAsand miRNAs were identified separately After the circRNA-related miRNAs dysregulated miRNAs were intersected 23miRNAs were selected for further study Gene functionanalysis including GO analysis and KEGG pathway analysiswas conducted for the targetmRNAs of the selectedmiRNAsThe results of KEGG pathway analysis indicated that p53signaling pathway and hippo signaling pathway were signifi-cantly enriched P53 is a well-known tumor suppressor geneand the p53 mutations have been reported in many cancers[32 33] In gastric cancer He et al [34] found that Fra-1 wasupregulated in gastric cancer tissues and played its functionby affecting the PI3KAkt and p53 signaling pathway Hippo
4 BioMed Research International
Samples
16
14
12
10
8
6
4
2
Nor
mal
ized
inte
nsity
val
ues
Nor
mal1
Nor
mal2
Nor
mal3
Nor
mal4
Nor
mal5
Nor
mal6
Tum
or1
Tum
or2
Tum
or3
Tum
or4
Tum
or5
Tum
or6
(a)
16
14
12
10
8
6
4
2161412108642
Gro
up-tu
mor
(nor
mal
ized
)
Group-normal (normalized)
(b)
0
2
4
6
8
10
12
minuslog10(P
val
ue)
minus5 0 5
log 2(fold change)Tumor versus normal
(c)
Tum
or1
Tum
or2
Tum
or3
Tum
or5
Tum
or4
Tum
or6
00 75 150
Nor
mal1
Nor
mal2
Nor
mal3
Nor
mal5
Nor
mal4
Nor
mal6
(d)
Figure 1 Differentially expressed circRNAs in tumor tissues and adjacent nontumor tissues from gastric cancer patients The box plot showsthe variations in circRNA expression (a) The scatter plot (b) and the volcano plot (c) illustrate the distributions of the data in the circRNAprofilesThe result from hierarchical clustering shows a distinguishable circRNA expression profiling among samplesThe heatmap shows thedifferentially expressed circRNAs in tumor and adjacent nontumor tissues (d) Each group consists of six samples Gene expression profilesare shown in rows ldquoRedrdquo indicates high relative expression and ldquobluerdquo indicates low relative expression
BioMed Research International 5
GSM
2452859
GSM
2452861
GSM
2452862
GSM
2452864
GSM
2452866
GSM
2452868
GSM
2452870
GSM
2452873
GSM
2452874
GSM
2452876
GSM
2452879
GSM
2452880
GSM
2452882
GSM
2452885
GSM
2452886
GSM
2452889
GSM
2452890
GSM
2452893
GSM
2452894
GSM
2452897
GSM
2452858
GSM
2452860
GSM
2452863
GSM
2452865
GSM
2452867
GSM
2452869
GSM
2452871
GSM
2452872
GSM
2452875
GSM
2452877
GSM
2452878
GSM
2452881
GSM
2452883
GSM
2452884
GSM
2452887
GSM
2452888
GSM
2452891
GSM
2452892
GSM
2452895
GSM
2452896
NormalCancer
3
2
1
0
minus1
minus2
minus3
Figure 2 Differentially expressed miRNAs in tumor tissues and adjacent normal tissues from gastric cancer patientsThe box plot shows thevariations in miRNA expression Each group consists of twenty samples
Differentially expressed miRNAs circRNA-related miRNAs
Selected miRNAs
88(32)
23(84)
164(596)
Figure 3 Based on differentially expressed miRNAs and circRNA-related miRNAs the overlapped 23 miRNAs were selected usingVenn graphing
signaling pathway is a newly discovered and conservedsignaling cascade first identified in drosophila [35] Hipposignal pathway regulates organ size control by governing cellproliferation and apoptosis and is reported to be a tumor-suppressive signal pathway As shown in Figure 5(b) CCND2is an important cross-talk gene associated with cell cycle p53signaling pathway and hippo signal pathway CCND2 alsohas a high betweenness centrality in the PPI network indicat-ing that CCND2might be a bridge of a lot of interactions Forexample CCND2 is a bridge of the target genes of hsa-miR-15a-5p and hsa-miR-93-5p Zhang et al [36] have reported
thatmiR-206 could inhibit gastric cancer proliferation in partby repressing CCND2 Meanwhile another study showedthat dysregulation of miR-206-CCND2 axis might contributeto the aggressive progression and poor prognosis of humangastric cancer in clinical settings Combined detection oftheir expressionmight be particularly helpful for surveillanceof disease progression and treatment stratification [37] How-ever the relationship between circRNA and CCND2 is stillunknown In the circRNA-miRNA-mRNA regulation net-work (Figure 6) we revealed that CCND2might be regulatedby hsa circRNA 105039 and hsa cirRNA 104682 throughhsa-miR-15a-5p and hsa circRNA 105039 separately We alsofound that hsa circRNA 101504 played a central role in theregulation network As circRNAs can serve as a competitiveendogenous RNA (ceRNA) to spongemiRNAs to regulate thetarget mRNAs [38 39] upregulation of hsa circRNA 101504might affect several mRNAs by downregulating hsa-miR-454-3p and hsa-miR-301a-3p In chondrosarcoma increasinghsa-miR-454-3p can downregulate Stat3 and Atg12 to inhibitchondrosarcoma growth [40] But in human glioma hsa-miR-454-3p has the opposite effect that the prognosis ofglioma with high hsa-miR-454-3p expression is significantlyworse compared with that of glioma with low hsa-miR-454-3p expression [41] Therefore more studies of hsa-miR-454-3p involved in gastric cancer are needed
5 Conclusion
In conclusion we have screened several dysregulated cir-cRNAs through microarray analysis and annotated their
6 BioMed Research International
0 1 2 3 4 5 6Anteriorposterior pattern specification
Liver developmentTranscription DNA-templated
Positive regulation of axon extensionNegative regulation of transcription from RNA polymerase II promoter
Regulation of cell adhesionMuscle cell differentiation
G1S transition of mitotic cell cycleRegulation of mRNA stability
Negative regulation of myeloid cell differentiationCytoplasmic stress granule
CytosolNucleoplasm
NucleusSpindle poleCentrosome
Cytoplasmic mRNA processing bodyMembrane
Microtubule associated complexCytoplasm
Protein bindingProtein kinase binding
Sequence-specific DNA bindingPoly(A) RNA binding
Transcriptional repressor activity RNA polymerase II core promoter proximal region sequence-specific bindingRNA binding
Kinase activityProtein phosphatase binding
Nuclear localization sequence bindingPhosphoprotein binding
minuslog(P value)
Figure 4The top 10 enrichment scores in gene ontology (GO) enrichment analysis on target genes of selectedmiRNAs Green bars representcell component terms Blue bars represent molecular function terms
0 05 1 15 2 25 3 35 4 45
Cell cycle
Oocyte meiosis
p53 signaling pathway
Axon guidance
Measles
MicroRNAs in cancer
Hepatitis B
Prostate cancer
Hippo signaling pathway
Neurotrophin signaling pathway
minuslog(P value)
(a)
PPP2CA
CCND2
CALM3
Oocyte meiosise
ZMAT3
ITPR3
p53 signaling pathway
CDC27
CDC25B
SESN3
WEE1
E2F3
CCNE1
CDKN1B
Cell cyclecyl
SEMA4C
Axon guidancei
LATS2
EPHA7
GSK3BPATJPARD6B
SLIT2
ROCK2
Hippo signaling nalipathway
(b)
Figure 5 The KEGG pathway enrichment analysis on target genes of the selected miRNAs (a) The top 10 enrichment scores in the KEGGpathway analysis of the target genes are shown (b) The network composed of the most enriched pathways and their related genes is shown
function in gastric cancer by bioinformatics analysis We willgather more clinical samples and validate our findings infuture work
Conflicts of Interest
The authors declare that there are no conflicts of interest
Authorsrsquo Contributions
Wei Gu and Ying Sun are equal contributors to this work
Acknowledgments
The authors thank CloudSeq Inc for the bioinformatic sup-port
BioMed Research International 7
Table 1 The list of differentially expressed genes involved in the PPI network (betweenness gt 4000)
Gene name Betweenness Degree Stress Closenesshsa-miR-27a-3p 1319550 24 46060 00015hsa-miR-15a-5p 1203950 19 30488 00015NUFIP2 725859 2 1440 00015hsa-miR-148a-3p 701686 18 67106 00013hsa-miR-17-5p 634249 13 156 00014BTG2 611600 2 160 00013hsa-miR-21-5p 606000 7 42 00011DCP2 560967 2 5090 00014ARAP2 538724 4 4698 00013hsa-miR-301a-3p 516847 22 52418 00013hsa circRNA 104682 438000 2 16344 00013YOD1 438000 2 160 00010hsa-miR-196a-5p 429800 15 14042 00009CCND2 428022 2 1440 00014hsa-miR-652-5p 416600 4 7722 00011
hsa_circRNA_104575
hsa-miR-145-5p
CCNJ
hsa-miR-181b-5p
BIRC6
ANGPT2
hsa_circRNA_101222
PKD2
DYNC1LI2
SQSTM1
hsa_circRNA_104697hsa-miR-28-5p
FOXJ3
hsa-miR-193a-3p
PUM1
CDKN1B
hsa_circRNA_104168 AGPAT3
PTPRG
HOXC8
hsa-miR-196a-5p
HAND1
PSMD11
hsa_circRNA_105049
IGF2BP1
PRTG
HOXA5
KHSRP
GSK3BHOXB8
MSI2
ACLY
SLC9A6
GATAD2B
HOXA9
hsa_circRNA_103552
hsa-miR-21-5p
YOD1
BTG2
CCDC126EPHA7GATA6
HOXB6
KLHL15 FRS2 PARD6B
ADNP
CUL5
HBP1
hsa_circRNA_104634
hsa-miR-145-3p
CDC25Bhsa_circRNA_100383
KIF1BRAB14
QKI
MTPN
hsa_circRNA_103349
hsa-miR-214-3phsa_circRNA_102064
hsa_circRNA_101858
CNIH1
GALNT7
hsa_circRNA_101017
ASB1
ITPR3
BHLHE41
ADAMTS5
hsa-miR-148a-3p
NPTX1 KIAA0226
NRARP
SPIRE1
ARL6IP1 FAM178A
hsa_circRNA_101504
PGM2L1
HPRT1
hsa-miR-301a-3p
B4GALT5
CNOT4
MLLT10
PPP6R1
SESTD1ATP11A
C7orf60
hsa-miR-17-5p
ROCK2
hsa-miR-93-5p
CAMTA1
EFCAB14
hsa_circRNA_104651
RGMB
ZNF148
PSAP
RRAGDPTPN4
ZMAT3hsa_circRNA_103840
TSHZ1
STEAP4ANKRD52
TBCEL
NPAT
ARAP2
BRWD1
hsa-miR-454-3p
SMARCD2
LDLR
PPP2CAhsa-miR-652-5p
RC3H2
hsa_circRNA_104682
MAP1B
hsa_circRNA_100583
LATS2
CEP85L
hsa-miR-135b-5p
SLC6A5
CENPB
MAN2A1
ZBTB18 ZNF800
LONP2LONRF2
ABL2 PEG10
PRDM4
SLIT2
DLL1
CBX4
hsa-miR-15a-5pTMEM245
CCNE1
DDX3XWEE1
CHAC1
ARL2
PAFAH1B1RB1CC1
ATP13A3
DCP2
NABP1
SPTY2D1
hsa-miR-27a-3p
SESN3
CDK16
RORA
JARID2
CHAMP1
NUFIP2
FAM118A
MFHAS1
SEMA4C
KPNA6
hsa_circRNA_104533
hsa-miR-125b-5pZC3H7B
hsa_circRNA_100319
KPNB1
hsa_circRNA_102700
TNPO1
hsa-miR-27b-3p
CALM3
RASL11B
hsa-miR-20b-5p
hsa_circRNA_105039
PFN2
TBC1D9TMEM167A
hsa-miR-18b-5p
hsa_circRNA_102062
INADL
TAOK1
GIGYF1
CAMK2N1
CADhsa-miR-18a-5p
RNF187
E2F3
CACUL1
CDC27ZNF367
SPRTN
CCND2
ARNTL2STAT3
MAP3K9
SUV420H1
ANKRD13C
PRR15
hsa-miR-660-3p
APH1A
hsa_circRNA_104374FAM98A
hsa_circRNA_100013
NOP9
mRNAmiRNAcircRNA
minus85 85
Fold change
0
Figure 6 The visualization of the circRNA-miRNA-mRNA regulation network The circular blue nodes represent mRNAs the diamondnodes represent the miRNAs and round rectangle nodes represent the circRNAs ldquoRedrdquo indicates high relative expression and ldquogreenrdquoindicates low relative expression
8 BioMed Research International
Supplementary Materials
Table S1 differentially expressed circRNAs between tumorandnormal tissues Table S2 differentially expressedmiRNAsbetween tumor and normal tissues (Supplementary Materi-als)
References
[1] L A Torre F Bray R L Siegel J Ferlay and J Lortet-Tieu-lent ldquoGlobal cancer statistics 2012rdquo CA A Cancer Journal forClinicians vol 65 no 2 pp 87ndash108 2015
[2] J Park do N J Thomas C Yoon and S S Yoon ldquoVascularendothelial growth factor a inhibition in gastric cancerrdquoGastricCancer vol 18 no 1 pp 33ndash42 2015
[3] P Karimi F Islami S Anandasabapathy N D Freedman andF Kamangar ldquoGastric cancer descriptive epidemiology riskfactors screening and preventionrdquo Cancer Epidemiology Bio-markers amp Prevention vol 23 no 5 pp 700ndash713 2014
[4] G Spolverato A Ejaz Y Kim et al ldquoRates and patterns of re-currence after curative intent resection for gastric cancerA United States multi-institutional analysisrdquo Journal of theAmerican College of Surgeons vol 219 no 4 pp 664ndash675 2014
[5] X Qi Y Liu W Wang et al ldquoManagement of advanced gastriccancer An overview of major findings from meta-analysisrdquoOncotarget vol 7 no 47 pp 78180ndash78205 2016
[6] J E Wilusz and P A Sharp ldquoA circuitous route to noncodingRNArdquo Science vol 340 no 6131 pp 440-441 2013
[7] W R Jeck J A Sorrentino K Wang et al ldquoCircular RNAs areabundant conserved and associated with ALU repeatsrdquo RNAvol 19 no 2 pp 141ndash157 2013
[8] S Memczak M Jens A Elefsinioti et al ldquoCircular RNAs area large class of animal RNAs with regulatory potencyrdquo Naturevol 495 no 7441 pp 333ndash338 2013
[9] Y-G Zhang H-L Yang Y Long and W-L Li ldquoCircularRNA in blood corpuscles combined with plasma protein factorfor early prediction of pre-eclampsiardquo BJOG An InternationalJournal of Obstetrics amp Gynaecology vol 123 no 13 pp 2113ndash2118 2016
[10] Y Li Q Zheng C Bao et al ldquoCircular RNA is enriched andstable in exosomes a promising biomarker for cancer diagno-sisrdquo Cell Research vol 25 no 8 pp 981ndash984 2015
[11] J Guarnerio M Bezzi J C Jeong et al ldquoOncogenic Role ofFusion-circRNAs Derived from Cancer-Associated Chromoso-mal Translocationsrdquo Cell vol 165 pp 289ndash302 2016
[12] X Wang Y Zhang L Huang et al ldquoDecreased expressionof hsa circ 001988 in colorectal cancer and its clinical sig-nificancesrdquo International Journal of Clinical and ExperimentalPathology vol 8 no 12 pp 16020ndash16025 2015
[13] MQin G Liu X Huo et al ldquoHsa circ 0001649 a circular RNAand potential novel biomarker for hepatocellular carcinomardquoCancer Biomarkers vol 16 no 1 pp 161ndash169 2016
[14] L Xuan L Qu H Zhou et al ldquoCircular RNA a novel bio-marker for progressive laryngeal cancerrdquo American Journal ofTranslational Research vol 8 no 2 pp 932ndash939 2016
[15] X Song N Zhang P Han et al ldquoCircular RNA profile in glio-mas revealed by identification tool UROBORUSrdquoNucleic AcidsResearch vol 44 no 9 article no e87 2016
[16] H L Sanger G Klotz D Riesner H J Gross and A KKleinschmidt ldquoViroids are single stranded covalently closedcircular RNA molecules existing as highly base paired rod like
structuresrdquo Proceedings of the National Acadamy of Sciences ofthe United States of America vol 73 no 11 pp 3852ndash3856 1976
[17] WWDu L FangW Yang et al ldquoInduction of tumor apoptosisthrough a circular RNA enhancing Foxo3 activityrdquoCell DeathampDifferentiation vol 24 no 2 pp 357ndash370 2017
[18] M Armakola M J Higgins M D Figley et al ldquoInhibition ofRNA lariat debranching enzyme suppresses TDP-43 toxicity inALS disease modelsrdquo Nature Genetics vol 44 no 12 pp 1302ndash1309 2012
[19] Z Li C Huang C Bao et al ldquoExon-intron circular RNAs regu-late transcription in the nucleusrdquoNature Structural ampMolecularBiology vol 22 no 3 pp 256ndash264 2015
[20] R Ashwal-Fluss M Meyer N R Pamudurti et al ldquoCircRNAbiogenesis competes with pre-mRNA splicingrdquo Molecular Cellvol 56 no 1 pp 55ndash66 2014
[21] WW Du W Yang E Liu Z Yang P Dhaliwal and B B YangldquoFoxo3 circular RNA retards cell cycle progression via formingternary complexes with p21 and CDK2rdquoNucleic Acids Researchvol 44 no 6 pp 2846ndash2858 2016
[22] A J Enright B John U Gaul T Tuschl C Sander and D SMarks ldquoMicroRNA targets inDrosophilardquoGenomeBiology vol5 no 1 2003
[23] A E Pasquinelli ldquoMicroRNAs and their targets recognitionregulation and an emerging reciprocal relationshiprdquo NatureReviews Genetics vol 13 no 4 pp 271ndash282 2012
[24] I S Vlachos M D Paraskevopoulou D Karagkouni et al ldquoDI-ANA-TarBase v70 indexing more than half a million experi-mentally supported miRNAmRNA interactionsrdquoNucleic AcidsResearch vol 43 no 1 pp D153ndashD159 2015
[25] G Bindea B Mlecnik H Hackl et al ldquoClueGO a Cytoscapeplug-in to decipher functionally grouped gene ontology andpathway annotation networksrdquoBioinformatics vol 25 no 8 pp1091ndash1093 2009
[26] G Bindea J Galon and B Mlecnik ldquoCluePedia Cytoscapeplugin pathway insights using integrated experimental and insilico datardquo Bioinformatics vol 29 no 5 pp 661ndash663 2013
[27] G Scardoni M Petterlini and C Laudanna ldquoAnalyzing biolog-ical network parameters with CentiScaPerdquo Bioinformatics vol25 no 21 pp 2857ndash2859 2009
[28] M B Assumpcao F C Moreira I G Hamoy et al ldquoHigh-throughput miRNA sequencing reveals a field effect in gastriccancer and suggests an epigenetic network mechanismrdquo Bioin-formatics and Biology Insights vol 9 pp 111ndash117 2015
[29] O Yang J Huang and S Lin ldquoRegulatory effects of miRNA ongastric cancer cellsrdquo Oncology Letters vol 8 no 2 pp 651ndash6562014
[30] S Meng H Zhou Z Feng et al ldquoCircRNA Functions andproperties of a novel potential biomarker for cancerrdquoMolecularCancer vol 16 no 1 article no 94 2017
[31] L Chen S Zhang J Wu et al ldquoCircRNA-100290 plays a role inoral cancer by functioning as a sponge of the MIR-29 familyrdquoOncogene vol 36 no 32 pp 4551ndash4561 2017
[32] A Saxena S K Shukla K N Prasad and U C Ghoshal ldquoAnal-ysis of p53 K-ras gene mutation Helicobacter pylori infectionin patients with gastric cancer peptic ulcer disease at a tertiarycare hospital in north Indiardquo Indian Journal ofMedical Researchvol 136 pp 664ndash670 2012
[33] S Uchino M Noguchi A Ochiai T Saito M Kobayashi andS Hirohashi ldquop53 mutation in gastric cancer a genetic modelfor carcinogenesis is common to gastric and colorectal cancerrdquoInternational Journal of Cancer vol 54 no 5 pp 759ndash764 1993
BioMed Research International 9
[34] J He G Zhu L Gao et al ldquoFra-1 is upregulated in gastric cancertissues and affects the PI3KAkt and p53 signaling pathway ingastric cancerrdquo International Journal of Oncology vol 47 no 5pp 1725ndash1734 2015
[35] T Zheng JWang H Jiang and L Liu ldquoHippo signaling in ovalcells and hepatocarcinogenesisrdquo Cancer Letters vol 302 no 2pp 91ndash99 2011
[36] L Zhang X Liu H Jin et al ldquoMiR-206 inhibits gastric cancerproliferation in part by repressing CyclinD2rdquo Cancer Lettersvol 332 no 1 pp 94ndash101 2013
[37] H Shi J Han S Yue T Zhang W Zhu and D Zhang ldquoProg-nostic significance of combined microRNA-206 and CyclinD2in gastric cancer patients after curative surgery A retrospectivecohort studyrdquo Biomedicine amp Pharmacotherapy vol 71 pp 210ndash215 2015
[38] D W Thomson and M E Dinger ldquoEndogenous microRNAsponges evidence and controversyrdquo Nature Reviews Geneticsvol 17 no 5 pp 272ndash283 2016
[39] L Salmena L Poliseno Y Tay L Kats and P P Pandolfi ldquoAceRNA hypothesis the rosetta stone of a hidden RNA lan-guagerdquo Cell vol 146 no 3 pp 353ndash358 2011
[40] X Bao T Ren Y Huang et al ldquoKnockdown of long non-codingRNA HOTAIR increases miR-454-3p by targeting Stat3 andAtg12 to inhibit chondrosarcoma growthrdquoCell DeathampDiseasevol 8 no 2 pp e2605ndashe2605 2017
[41] N Shao L Wang L Xue R Wang and Q Lan ldquoPlasma miR-454-3p as a potential prognostic indicator in human gliomardquoNeurological Sciences vol 36 no 2 pp 309ndash313 2015
Stem Cells International
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
MEDIATORSINFLAMMATION
of
EndocrinologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Disease Markers
Hindawiwwwhindawicom Volume 2018
BioMed Research International
OncologyJournal of
Hindawiwwwhindawicom Volume 2013
Hindawiwwwhindawicom Volume 2018
Oxidative Medicine and Cellular Longevity
Hindawiwwwhindawicom Volume 2018
PPAR Research
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018
Immunology ResearchHindawiwwwhindawicom Volume 2018
Journal of
ObesityJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Computational and Mathematical Methods in Medicine
Hindawiwwwhindawicom Volume 2018
Behavioural Neurology
OphthalmologyJournal of
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Diabetes ResearchJournal of
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Research and TreatmentAIDS
Hindawiwwwhindawicom Volume 2018
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Hindawiwwwhindawicom Volume 2018
Parkinsonrsquos Disease
Evidence-Based Complementary andAlternative Medicine
Volume 2018Hindawiwwwhindawicom
Submit your manuscripts atwwwhindawicom
4 BioMed Research International
Samples
16
14
12
10
8
6
4
2
Nor
mal
ized
inte
nsity
val
ues
Nor
mal1
Nor
mal2
Nor
mal3
Nor
mal4
Nor
mal5
Nor
mal6
Tum
or1
Tum
or2
Tum
or3
Tum
or4
Tum
or5
Tum
or6
(a)
16
14
12
10
8
6
4
2161412108642
Gro
up-tu
mor
(nor
mal
ized
)
Group-normal (normalized)
(b)
0
2
4
6
8
10
12
minuslog10(P
val
ue)
minus5 0 5
log 2(fold change)Tumor versus normal
(c)
Tum
or1
Tum
or2
Tum
or3
Tum
or5
Tum
or4
Tum
or6
00 75 150
Nor
mal1
Nor
mal2
Nor
mal3
Nor
mal5
Nor
mal4
Nor
mal6
(d)
Figure 1 Differentially expressed circRNAs in tumor tissues and adjacent nontumor tissues from gastric cancer patients The box plot showsthe variations in circRNA expression (a) The scatter plot (b) and the volcano plot (c) illustrate the distributions of the data in the circRNAprofilesThe result from hierarchical clustering shows a distinguishable circRNA expression profiling among samplesThe heatmap shows thedifferentially expressed circRNAs in tumor and adjacent nontumor tissues (d) Each group consists of six samples Gene expression profilesare shown in rows ldquoRedrdquo indicates high relative expression and ldquobluerdquo indicates low relative expression
BioMed Research International 5
GSM
2452859
GSM
2452861
GSM
2452862
GSM
2452864
GSM
2452866
GSM
2452868
GSM
2452870
GSM
2452873
GSM
2452874
GSM
2452876
GSM
2452879
GSM
2452880
GSM
2452882
GSM
2452885
GSM
2452886
GSM
2452889
GSM
2452890
GSM
2452893
GSM
2452894
GSM
2452897
GSM
2452858
GSM
2452860
GSM
2452863
GSM
2452865
GSM
2452867
GSM
2452869
GSM
2452871
GSM
2452872
GSM
2452875
GSM
2452877
GSM
2452878
GSM
2452881
GSM
2452883
GSM
2452884
GSM
2452887
GSM
2452888
GSM
2452891
GSM
2452892
GSM
2452895
GSM
2452896
NormalCancer
3
2
1
0
minus1
minus2
minus3
Figure 2 Differentially expressed miRNAs in tumor tissues and adjacent normal tissues from gastric cancer patientsThe box plot shows thevariations in miRNA expression Each group consists of twenty samples
Differentially expressed miRNAs circRNA-related miRNAs
Selected miRNAs
88(32)
23(84)
164(596)
Figure 3 Based on differentially expressed miRNAs and circRNA-related miRNAs the overlapped 23 miRNAs were selected usingVenn graphing
signaling pathway is a newly discovered and conservedsignaling cascade first identified in drosophila [35] Hipposignal pathway regulates organ size control by governing cellproliferation and apoptosis and is reported to be a tumor-suppressive signal pathway As shown in Figure 5(b) CCND2is an important cross-talk gene associated with cell cycle p53signaling pathway and hippo signal pathway CCND2 alsohas a high betweenness centrality in the PPI network indicat-ing that CCND2might be a bridge of a lot of interactions Forexample CCND2 is a bridge of the target genes of hsa-miR-15a-5p and hsa-miR-93-5p Zhang et al [36] have reported
thatmiR-206 could inhibit gastric cancer proliferation in partby repressing CCND2 Meanwhile another study showedthat dysregulation of miR-206-CCND2 axis might contributeto the aggressive progression and poor prognosis of humangastric cancer in clinical settings Combined detection oftheir expressionmight be particularly helpful for surveillanceof disease progression and treatment stratification [37] How-ever the relationship between circRNA and CCND2 is stillunknown In the circRNA-miRNA-mRNA regulation net-work (Figure 6) we revealed that CCND2might be regulatedby hsa circRNA 105039 and hsa cirRNA 104682 throughhsa-miR-15a-5p and hsa circRNA 105039 separately We alsofound that hsa circRNA 101504 played a central role in theregulation network As circRNAs can serve as a competitiveendogenous RNA (ceRNA) to spongemiRNAs to regulate thetarget mRNAs [38 39] upregulation of hsa circRNA 101504might affect several mRNAs by downregulating hsa-miR-454-3p and hsa-miR-301a-3p In chondrosarcoma increasinghsa-miR-454-3p can downregulate Stat3 and Atg12 to inhibitchondrosarcoma growth [40] But in human glioma hsa-miR-454-3p has the opposite effect that the prognosis ofglioma with high hsa-miR-454-3p expression is significantlyworse compared with that of glioma with low hsa-miR-454-3p expression [41] Therefore more studies of hsa-miR-454-3p involved in gastric cancer are needed
5 Conclusion
In conclusion we have screened several dysregulated cir-cRNAs through microarray analysis and annotated their
6 BioMed Research International
0 1 2 3 4 5 6Anteriorposterior pattern specification
Liver developmentTranscription DNA-templated
Positive regulation of axon extensionNegative regulation of transcription from RNA polymerase II promoter
Regulation of cell adhesionMuscle cell differentiation
G1S transition of mitotic cell cycleRegulation of mRNA stability
Negative regulation of myeloid cell differentiationCytoplasmic stress granule
CytosolNucleoplasm
NucleusSpindle poleCentrosome
Cytoplasmic mRNA processing bodyMembrane
Microtubule associated complexCytoplasm
Protein bindingProtein kinase binding
Sequence-specific DNA bindingPoly(A) RNA binding
Transcriptional repressor activity RNA polymerase II core promoter proximal region sequence-specific bindingRNA binding
Kinase activityProtein phosphatase binding
Nuclear localization sequence bindingPhosphoprotein binding
minuslog(P value)
Figure 4The top 10 enrichment scores in gene ontology (GO) enrichment analysis on target genes of selectedmiRNAs Green bars representcell component terms Blue bars represent molecular function terms
0 05 1 15 2 25 3 35 4 45
Cell cycle
Oocyte meiosis
p53 signaling pathway
Axon guidance
Measles
MicroRNAs in cancer
Hepatitis B
Prostate cancer
Hippo signaling pathway
Neurotrophin signaling pathway
minuslog(P value)
(a)
PPP2CA
CCND2
CALM3
Oocyte meiosise
ZMAT3
ITPR3
p53 signaling pathway
CDC27
CDC25B
SESN3
WEE1
E2F3
CCNE1
CDKN1B
Cell cyclecyl
SEMA4C
Axon guidancei
LATS2
EPHA7
GSK3BPATJPARD6B
SLIT2
ROCK2
Hippo signaling nalipathway
(b)
Figure 5 The KEGG pathway enrichment analysis on target genes of the selected miRNAs (a) The top 10 enrichment scores in the KEGGpathway analysis of the target genes are shown (b) The network composed of the most enriched pathways and their related genes is shown
function in gastric cancer by bioinformatics analysis We willgather more clinical samples and validate our findings infuture work
Conflicts of Interest
The authors declare that there are no conflicts of interest
Authorsrsquo Contributions
Wei Gu and Ying Sun are equal contributors to this work
Acknowledgments
The authors thank CloudSeq Inc for the bioinformatic sup-port
BioMed Research International 7
Table 1 The list of differentially expressed genes involved in the PPI network (betweenness gt 4000)
Gene name Betweenness Degree Stress Closenesshsa-miR-27a-3p 1319550 24 46060 00015hsa-miR-15a-5p 1203950 19 30488 00015NUFIP2 725859 2 1440 00015hsa-miR-148a-3p 701686 18 67106 00013hsa-miR-17-5p 634249 13 156 00014BTG2 611600 2 160 00013hsa-miR-21-5p 606000 7 42 00011DCP2 560967 2 5090 00014ARAP2 538724 4 4698 00013hsa-miR-301a-3p 516847 22 52418 00013hsa circRNA 104682 438000 2 16344 00013YOD1 438000 2 160 00010hsa-miR-196a-5p 429800 15 14042 00009CCND2 428022 2 1440 00014hsa-miR-652-5p 416600 4 7722 00011
hsa_circRNA_104575
hsa-miR-145-5p
CCNJ
hsa-miR-181b-5p
BIRC6
ANGPT2
hsa_circRNA_101222
PKD2
DYNC1LI2
SQSTM1
hsa_circRNA_104697hsa-miR-28-5p
FOXJ3
hsa-miR-193a-3p
PUM1
CDKN1B
hsa_circRNA_104168 AGPAT3
PTPRG
HOXC8
hsa-miR-196a-5p
HAND1
PSMD11
hsa_circRNA_105049
IGF2BP1
PRTG
HOXA5
KHSRP
GSK3BHOXB8
MSI2
ACLY
SLC9A6
GATAD2B
HOXA9
hsa_circRNA_103552
hsa-miR-21-5p
YOD1
BTG2
CCDC126EPHA7GATA6
HOXB6
KLHL15 FRS2 PARD6B
ADNP
CUL5
HBP1
hsa_circRNA_104634
hsa-miR-145-3p
CDC25Bhsa_circRNA_100383
KIF1BRAB14
QKI
MTPN
hsa_circRNA_103349
hsa-miR-214-3phsa_circRNA_102064
hsa_circRNA_101858
CNIH1
GALNT7
hsa_circRNA_101017
ASB1
ITPR3
BHLHE41
ADAMTS5
hsa-miR-148a-3p
NPTX1 KIAA0226
NRARP
SPIRE1
ARL6IP1 FAM178A
hsa_circRNA_101504
PGM2L1
HPRT1
hsa-miR-301a-3p
B4GALT5
CNOT4
MLLT10
PPP6R1
SESTD1ATP11A
C7orf60
hsa-miR-17-5p
ROCK2
hsa-miR-93-5p
CAMTA1
EFCAB14
hsa_circRNA_104651
RGMB
ZNF148
PSAP
RRAGDPTPN4
ZMAT3hsa_circRNA_103840
TSHZ1
STEAP4ANKRD52
TBCEL
NPAT
ARAP2
BRWD1
hsa-miR-454-3p
SMARCD2
LDLR
PPP2CAhsa-miR-652-5p
RC3H2
hsa_circRNA_104682
MAP1B
hsa_circRNA_100583
LATS2
CEP85L
hsa-miR-135b-5p
SLC6A5
CENPB
MAN2A1
ZBTB18 ZNF800
LONP2LONRF2
ABL2 PEG10
PRDM4
SLIT2
DLL1
CBX4
hsa-miR-15a-5pTMEM245
CCNE1
DDX3XWEE1
CHAC1
ARL2
PAFAH1B1RB1CC1
ATP13A3
DCP2
NABP1
SPTY2D1
hsa-miR-27a-3p
SESN3
CDK16
RORA
JARID2
CHAMP1
NUFIP2
FAM118A
MFHAS1
SEMA4C
KPNA6
hsa_circRNA_104533
hsa-miR-125b-5pZC3H7B
hsa_circRNA_100319
KPNB1
hsa_circRNA_102700
TNPO1
hsa-miR-27b-3p
CALM3
RASL11B
hsa-miR-20b-5p
hsa_circRNA_105039
PFN2
TBC1D9TMEM167A
hsa-miR-18b-5p
hsa_circRNA_102062
INADL
TAOK1
GIGYF1
CAMK2N1
CADhsa-miR-18a-5p
RNF187
E2F3
CACUL1
CDC27ZNF367
SPRTN
CCND2
ARNTL2STAT3
MAP3K9
SUV420H1
ANKRD13C
PRR15
hsa-miR-660-3p
APH1A
hsa_circRNA_104374FAM98A
hsa_circRNA_100013
NOP9
mRNAmiRNAcircRNA
minus85 85
Fold change
0
Figure 6 The visualization of the circRNA-miRNA-mRNA regulation network The circular blue nodes represent mRNAs the diamondnodes represent the miRNAs and round rectangle nodes represent the circRNAs ldquoRedrdquo indicates high relative expression and ldquogreenrdquoindicates low relative expression
8 BioMed Research International
Supplementary Materials
Table S1 differentially expressed circRNAs between tumorandnormal tissues Table S2 differentially expressedmiRNAsbetween tumor and normal tissues (Supplementary Materi-als)
References
[1] L A Torre F Bray R L Siegel J Ferlay and J Lortet-Tieu-lent ldquoGlobal cancer statistics 2012rdquo CA A Cancer Journal forClinicians vol 65 no 2 pp 87ndash108 2015
[2] J Park do N J Thomas C Yoon and S S Yoon ldquoVascularendothelial growth factor a inhibition in gastric cancerrdquoGastricCancer vol 18 no 1 pp 33ndash42 2015
[3] P Karimi F Islami S Anandasabapathy N D Freedman andF Kamangar ldquoGastric cancer descriptive epidemiology riskfactors screening and preventionrdquo Cancer Epidemiology Bio-markers amp Prevention vol 23 no 5 pp 700ndash713 2014
[4] G Spolverato A Ejaz Y Kim et al ldquoRates and patterns of re-currence after curative intent resection for gastric cancerA United States multi-institutional analysisrdquo Journal of theAmerican College of Surgeons vol 219 no 4 pp 664ndash675 2014
[5] X Qi Y Liu W Wang et al ldquoManagement of advanced gastriccancer An overview of major findings from meta-analysisrdquoOncotarget vol 7 no 47 pp 78180ndash78205 2016
[6] J E Wilusz and P A Sharp ldquoA circuitous route to noncodingRNArdquo Science vol 340 no 6131 pp 440-441 2013
[7] W R Jeck J A Sorrentino K Wang et al ldquoCircular RNAs areabundant conserved and associated with ALU repeatsrdquo RNAvol 19 no 2 pp 141ndash157 2013
[8] S Memczak M Jens A Elefsinioti et al ldquoCircular RNAs area large class of animal RNAs with regulatory potencyrdquo Naturevol 495 no 7441 pp 333ndash338 2013
[9] Y-G Zhang H-L Yang Y Long and W-L Li ldquoCircularRNA in blood corpuscles combined with plasma protein factorfor early prediction of pre-eclampsiardquo BJOG An InternationalJournal of Obstetrics amp Gynaecology vol 123 no 13 pp 2113ndash2118 2016
[10] Y Li Q Zheng C Bao et al ldquoCircular RNA is enriched andstable in exosomes a promising biomarker for cancer diagno-sisrdquo Cell Research vol 25 no 8 pp 981ndash984 2015
[11] J Guarnerio M Bezzi J C Jeong et al ldquoOncogenic Role ofFusion-circRNAs Derived from Cancer-Associated Chromoso-mal Translocationsrdquo Cell vol 165 pp 289ndash302 2016
[12] X Wang Y Zhang L Huang et al ldquoDecreased expressionof hsa circ 001988 in colorectal cancer and its clinical sig-nificancesrdquo International Journal of Clinical and ExperimentalPathology vol 8 no 12 pp 16020ndash16025 2015
[13] MQin G Liu X Huo et al ldquoHsa circ 0001649 a circular RNAand potential novel biomarker for hepatocellular carcinomardquoCancer Biomarkers vol 16 no 1 pp 161ndash169 2016
[14] L Xuan L Qu H Zhou et al ldquoCircular RNA a novel bio-marker for progressive laryngeal cancerrdquo American Journal ofTranslational Research vol 8 no 2 pp 932ndash939 2016
[15] X Song N Zhang P Han et al ldquoCircular RNA profile in glio-mas revealed by identification tool UROBORUSrdquoNucleic AcidsResearch vol 44 no 9 article no e87 2016
[16] H L Sanger G Klotz D Riesner H J Gross and A KKleinschmidt ldquoViroids are single stranded covalently closedcircular RNA molecules existing as highly base paired rod like
structuresrdquo Proceedings of the National Acadamy of Sciences ofthe United States of America vol 73 no 11 pp 3852ndash3856 1976
[17] WWDu L FangW Yang et al ldquoInduction of tumor apoptosisthrough a circular RNA enhancing Foxo3 activityrdquoCell DeathampDifferentiation vol 24 no 2 pp 357ndash370 2017
[18] M Armakola M J Higgins M D Figley et al ldquoInhibition ofRNA lariat debranching enzyme suppresses TDP-43 toxicity inALS disease modelsrdquo Nature Genetics vol 44 no 12 pp 1302ndash1309 2012
[19] Z Li C Huang C Bao et al ldquoExon-intron circular RNAs regu-late transcription in the nucleusrdquoNature Structural ampMolecularBiology vol 22 no 3 pp 256ndash264 2015
[20] R Ashwal-Fluss M Meyer N R Pamudurti et al ldquoCircRNAbiogenesis competes with pre-mRNA splicingrdquo Molecular Cellvol 56 no 1 pp 55ndash66 2014
[21] WW Du W Yang E Liu Z Yang P Dhaliwal and B B YangldquoFoxo3 circular RNA retards cell cycle progression via formingternary complexes with p21 and CDK2rdquoNucleic Acids Researchvol 44 no 6 pp 2846ndash2858 2016
[22] A J Enright B John U Gaul T Tuschl C Sander and D SMarks ldquoMicroRNA targets inDrosophilardquoGenomeBiology vol5 no 1 2003
[23] A E Pasquinelli ldquoMicroRNAs and their targets recognitionregulation and an emerging reciprocal relationshiprdquo NatureReviews Genetics vol 13 no 4 pp 271ndash282 2012
[24] I S Vlachos M D Paraskevopoulou D Karagkouni et al ldquoDI-ANA-TarBase v70 indexing more than half a million experi-mentally supported miRNAmRNA interactionsrdquoNucleic AcidsResearch vol 43 no 1 pp D153ndashD159 2015
[25] G Bindea B Mlecnik H Hackl et al ldquoClueGO a Cytoscapeplug-in to decipher functionally grouped gene ontology andpathway annotation networksrdquoBioinformatics vol 25 no 8 pp1091ndash1093 2009
[26] G Bindea J Galon and B Mlecnik ldquoCluePedia Cytoscapeplugin pathway insights using integrated experimental and insilico datardquo Bioinformatics vol 29 no 5 pp 661ndash663 2013
[27] G Scardoni M Petterlini and C Laudanna ldquoAnalyzing biolog-ical network parameters with CentiScaPerdquo Bioinformatics vol25 no 21 pp 2857ndash2859 2009
[28] M B Assumpcao F C Moreira I G Hamoy et al ldquoHigh-throughput miRNA sequencing reveals a field effect in gastriccancer and suggests an epigenetic network mechanismrdquo Bioin-formatics and Biology Insights vol 9 pp 111ndash117 2015
[29] O Yang J Huang and S Lin ldquoRegulatory effects of miRNA ongastric cancer cellsrdquo Oncology Letters vol 8 no 2 pp 651ndash6562014
[30] S Meng H Zhou Z Feng et al ldquoCircRNA Functions andproperties of a novel potential biomarker for cancerrdquoMolecularCancer vol 16 no 1 article no 94 2017
[31] L Chen S Zhang J Wu et al ldquoCircRNA-100290 plays a role inoral cancer by functioning as a sponge of the MIR-29 familyrdquoOncogene vol 36 no 32 pp 4551ndash4561 2017
[32] A Saxena S K Shukla K N Prasad and U C Ghoshal ldquoAnal-ysis of p53 K-ras gene mutation Helicobacter pylori infectionin patients with gastric cancer peptic ulcer disease at a tertiarycare hospital in north Indiardquo Indian Journal ofMedical Researchvol 136 pp 664ndash670 2012
[33] S Uchino M Noguchi A Ochiai T Saito M Kobayashi andS Hirohashi ldquop53 mutation in gastric cancer a genetic modelfor carcinogenesis is common to gastric and colorectal cancerrdquoInternational Journal of Cancer vol 54 no 5 pp 759ndash764 1993
BioMed Research International 9
[34] J He G Zhu L Gao et al ldquoFra-1 is upregulated in gastric cancertissues and affects the PI3KAkt and p53 signaling pathway ingastric cancerrdquo International Journal of Oncology vol 47 no 5pp 1725ndash1734 2015
[35] T Zheng JWang H Jiang and L Liu ldquoHippo signaling in ovalcells and hepatocarcinogenesisrdquo Cancer Letters vol 302 no 2pp 91ndash99 2011
[36] L Zhang X Liu H Jin et al ldquoMiR-206 inhibits gastric cancerproliferation in part by repressing CyclinD2rdquo Cancer Lettersvol 332 no 1 pp 94ndash101 2013
[37] H Shi J Han S Yue T Zhang W Zhu and D Zhang ldquoProg-nostic significance of combined microRNA-206 and CyclinD2in gastric cancer patients after curative surgery A retrospectivecohort studyrdquo Biomedicine amp Pharmacotherapy vol 71 pp 210ndash215 2015
[38] D W Thomson and M E Dinger ldquoEndogenous microRNAsponges evidence and controversyrdquo Nature Reviews Geneticsvol 17 no 5 pp 272ndash283 2016
[39] L Salmena L Poliseno Y Tay L Kats and P P Pandolfi ldquoAceRNA hypothesis the rosetta stone of a hidden RNA lan-guagerdquo Cell vol 146 no 3 pp 353ndash358 2011
[40] X Bao T Ren Y Huang et al ldquoKnockdown of long non-codingRNA HOTAIR increases miR-454-3p by targeting Stat3 andAtg12 to inhibit chondrosarcoma growthrdquoCell DeathampDiseasevol 8 no 2 pp e2605ndashe2605 2017
[41] N Shao L Wang L Xue R Wang and Q Lan ldquoPlasma miR-454-3p as a potential prognostic indicator in human gliomardquoNeurological Sciences vol 36 no 2 pp 309ndash313 2015
Stem Cells International
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
MEDIATORSINFLAMMATION
of
EndocrinologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Disease Markers
Hindawiwwwhindawicom Volume 2018
BioMed Research International
OncologyJournal of
Hindawiwwwhindawicom Volume 2013
Hindawiwwwhindawicom Volume 2018
Oxidative Medicine and Cellular Longevity
Hindawiwwwhindawicom Volume 2018
PPAR Research
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018
Immunology ResearchHindawiwwwhindawicom Volume 2018
Journal of
ObesityJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Computational and Mathematical Methods in Medicine
Hindawiwwwhindawicom Volume 2018
Behavioural Neurology
OphthalmologyJournal of
Hindawiwwwhindawicom Volume 2018
Diabetes ResearchJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Research and TreatmentAIDS
Hindawiwwwhindawicom Volume 2018
Gastroenterology Research and Practice
Hindawiwwwhindawicom Volume 2018
Parkinsonrsquos Disease
Evidence-Based Complementary andAlternative Medicine
Volume 2018Hindawiwwwhindawicom
Submit your manuscripts atwwwhindawicom
BioMed Research International 5
GSM
2452859
GSM
2452861
GSM
2452862
GSM
2452864
GSM
2452866
GSM
2452868
GSM
2452870
GSM
2452873
GSM
2452874
GSM
2452876
GSM
2452879
GSM
2452880
GSM
2452882
GSM
2452885
GSM
2452886
GSM
2452889
GSM
2452890
GSM
2452893
GSM
2452894
GSM
2452897
GSM
2452858
GSM
2452860
GSM
2452863
GSM
2452865
GSM
2452867
GSM
2452869
GSM
2452871
GSM
2452872
GSM
2452875
GSM
2452877
GSM
2452878
GSM
2452881
GSM
2452883
GSM
2452884
GSM
2452887
GSM
2452888
GSM
2452891
GSM
2452892
GSM
2452895
GSM
2452896
NormalCancer
3
2
1
0
minus1
minus2
minus3
Figure 2 Differentially expressed miRNAs in tumor tissues and adjacent normal tissues from gastric cancer patientsThe box plot shows thevariations in miRNA expression Each group consists of twenty samples
Differentially expressed miRNAs circRNA-related miRNAs
Selected miRNAs
88(32)
23(84)
164(596)
Figure 3 Based on differentially expressed miRNAs and circRNA-related miRNAs the overlapped 23 miRNAs were selected usingVenn graphing
signaling pathway is a newly discovered and conservedsignaling cascade first identified in drosophila [35] Hipposignal pathway regulates organ size control by governing cellproliferation and apoptosis and is reported to be a tumor-suppressive signal pathway As shown in Figure 5(b) CCND2is an important cross-talk gene associated with cell cycle p53signaling pathway and hippo signal pathway CCND2 alsohas a high betweenness centrality in the PPI network indicat-ing that CCND2might be a bridge of a lot of interactions Forexample CCND2 is a bridge of the target genes of hsa-miR-15a-5p and hsa-miR-93-5p Zhang et al [36] have reported
thatmiR-206 could inhibit gastric cancer proliferation in partby repressing CCND2 Meanwhile another study showedthat dysregulation of miR-206-CCND2 axis might contributeto the aggressive progression and poor prognosis of humangastric cancer in clinical settings Combined detection oftheir expressionmight be particularly helpful for surveillanceof disease progression and treatment stratification [37] How-ever the relationship between circRNA and CCND2 is stillunknown In the circRNA-miRNA-mRNA regulation net-work (Figure 6) we revealed that CCND2might be regulatedby hsa circRNA 105039 and hsa cirRNA 104682 throughhsa-miR-15a-5p and hsa circRNA 105039 separately We alsofound that hsa circRNA 101504 played a central role in theregulation network As circRNAs can serve as a competitiveendogenous RNA (ceRNA) to spongemiRNAs to regulate thetarget mRNAs [38 39] upregulation of hsa circRNA 101504might affect several mRNAs by downregulating hsa-miR-454-3p and hsa-miR-301a-3p In chondrosarcoma increasinghsa-miR-454-3p can downregulate Stat3 and Atg12 to inhibitchondrosarcoma growth [40] But in human glioma hsa-miR-454-3p has the opposite effect that the prognosis ofglioma with high hsa-miR-454-3p expression is significantlyworse compared with that of glioma with low hsa-miR-454-3p expression [41] Therefore more studies of hsa-miR-454-3p involved in gastric cancer are needed
5 Conclusion
In conclusion we have screened several dysregulated cir-cRNAs through microarray analysis and annotated their
6 BioMed Research International
0 1 2 3 4 5 6Anteriorposterior pattern specification
Liver developmentTranscription DNA-templated
Positive regulation of axon extensionNegative regulation of transcription from RNA polymerase II promoter
Regulation of cell adhesionMuscle cell differentiation
G1S transition of mitotic cell cycleRegulation of mRNA stability
Negative regulation of myeloid cell differentiationCytoplasmic stress granule
CytosolNucleoplasm
NucleusSpindle poleCentrosome
Cytoplasmic mRNA processing bodyMembrane
Microtubule associated complexCytoplasm
Protein bindingProtein kinase binding
Sequence-specific DNA bindingPoly(A) RNA binding
Transcriptional repressor activity RNA polymerase II core promoter proximal region sequence-specific bindingRNA binding
Kinase activityProtein phosphatase binding
Nuclear localization sequence bindingPhosphoprotein binding
minuslog(P value)
Figure 4The top 10 enrichment scores in gene ontology (GO) enrichment analysis on target genes of selectedmiRNAs Green bars representcell component terms Blue bars represent molecular function terms
0 05 1 15 2 25 3 35 4 45
Cell cycle
Oocyte meiosis
p53 signaling pathway
Axon guidance
Measles
MicroRNAs in cancer
Hepatitis B
Prostate cancer
Hippo signaling pathway
Neurotrophin signaling pathway
minuslog(P value)
(a)
PPP2CA
CCND2
CALM3
Oocyte meiosise
ZMAT3
ITPR3
p53 signaling pathway
CDC27
CDC25B
SESN3
WEE1
E2F3
CCNE1
CDKN1B
Cell cyclecyl
SEMA4C
Axon guidancei
LATS2
EPHA7
GSK3BPATJPARD6B
SLIT2
ROCK2
Hippo signaling nalipathway
(b)
Figure 5 The KEGG pathway enrichment analysis on target genes of the selected miRNAs (a) The top 10 enrichment scores in the KEGGpathway analysis of the target genes are shown (b) The network composed of the most enriched pathways and their related genes is shown
function in gastric cancer by bioinformatics analysis We willgather more clinical samples and validate our findings infuture work
Conflicts of Interest
The authors declare that there are no conflicts of interest
Authorsrsquo Contributions
Wei Gu and Ying Sun are equal contributors to this work
Acknowledgments
The authors thank CloudSeq Inc for the bioinformatic sup-port
BioMed Research International 7
Table 1 The list of differentially expressed genes involved in the PPI network (betweenness gt 4000)
Gene name Betweenness Degree Stress Closenesshsa-miR-27a-3p 1319550 24 46060 00015hsa-miR-15a-5p 1203950 19 30488 00015NUFIP2 725859 2 1440 00015hsa-miR-148a-3p 701686 18 67106 00013hsa-miR-17-5p 634249 13 156 00014BTG2 611600 2 160 00013hsa-miR-21-5p 606000 7 42 00011DCP2 560967 2 5090 00014ARAP2 538724 4 4698 00013hsa-miR-301a-3p 516847 22 52418 00013hsa circRNA 104682 438000 2 16344 00013YOD1 438000 2 160 00010hsa-miR-196a-5p 429800 15 14042 00009CCND2 428022 2 1440 00014hsa-miR-652-5p 416600 4 7722 00011
hsa_circRNA_104575
hsa-miR-145-5p
CCNJ
hsa-miR-181b-5p
BIRC6
ANGPT2
hsa_circRNA_101222
PKD2
DYNC1LI2
SQSTM1
hsa_circRNA_104697hsa-miR-28-5p
FOXJ3
hsa-miR-193a-3p
PUM1
CDKN1B
hsa_circRNA_104168 AGPAT3
PTPRG
HOXC8
hsa-miR-196a-5p
HAND1
PSMD11
hsa_circRNA_105049
IGF2BP1
PRTG
HOXA5
KHSRP
GSK3BHOXB8
MSI2
ACLY
SLC9A6
GATAD2B
HOXA9
hsa_circRNA_103552
hsa-miR-21-5p
YOD1
BTG2
CCDC126EPHA7GATA6
HOXB6
KLHL15 FRS2 PARD6B
ADNP
CUL5
HBP1
hsa_circRNA_104634
hsa-miR-145-3p
CDC25Bhsa_circRNA_100383
KIF1BRAB14
QKI
MTPN
hsa_circRNA_103349
hsa-miR-214-3phsa_circRNA_102064
hsa_circRNA_101858
CNIH1
GALNT7
hsa_circRNA_101017
ASB1
ITPR3
BHLHE41
ADAMTS5
hsa-miR-148a-3p
NPTX1 KIAA0226
NRARP
SPIRE1
ARL6IP1 FAM178A
hsa_circRNA_101504
PGM2L1
HPRT1
hsa-miR-301a-3p
B4GALT5
CNOT4
MLLT10
PPP6R1
SESTD1ATP11A
C7orf60
hsa-miR-17-5p
ROCK2
hsa-miR-93-5p
CAMTA1
EFCAB14
hsa_circRNA_104651
RGMB
ZNF148
PSAP
RRAGDPTPN4
ZMAT3hsa_circRNA_103840
TSHZ1
STEAP4ANKRD52
TBCEL
NPAT
ARAP2
BRWD1
hsa-miR-454-3p
SMARCD2
LDLR
PPP2CAhsa-miR-652-5p
RC3H2
hsa_circRNA_104682
MAP1B
hsa_circRNA_100583
LATS2
CEP85L
hsa-miR-135b-5p
SLC6A5
CENPB
MAN2A1
ZBTB18 ZNF800
LONP2LONRF2
ABL2 PEG10
PRDM4
SLIT2
DLL1
CBX4
hsa-miR-15a-5pTMEM245
CCNE1
DDX3XWEE1
CHAC1
ARL2
PAFAH1B1RB1CC1
ATP13A3
DCP2
NABP1
SPTY2D1
hsa-miR-27a-3p
SESN3
CDK16
RORA
JARID2
CHAMP1
NUFIP2
FAM118A
MFHAS1
SEMA4C
KPNA6
hsa_circRNA_104533
hsa-miR-125b-5pZC3H7B
hsa_circRNA_100319
KPNB1
hsa_circRNA_102700
TNPO1
hsa-miR-27b-3p
CALM3
RASL11B
hsa-miR-20b-5p
hsa_circRNA_105039
PFN2
TBC1D9TMEM167A
hsa-miR-18b-5p
hsa_circRNA_102062
INADL
TAOK1
GIGYF1
CAMK2N1
CADhsa-miR-18a-5p
RNF187
E2F3
CACUL1
CDC27ZNF367
SPRTN
CCND2
ARNTL2STAT3
MAP3K9
SUV420H1
ANKRD13C
PRR15
hsa-miR-660-3p
APH1A
hsa_circRNA_104374FAM98A
hsa_circRNA_100013
NOP9
mRNAmiRNAcircRNA
minus85 85
Fold change
0
Figure 6 The visualization of the circRNA-miRNA-mRNA regulation network The circular blue nodes represent mRNAs the diamondnodes represent the miRNAs and round rectangle nodes represent the circRNAs ldquoRedrdquo indicates high relative expression and ldquogreenrdquoindicates low relative expression
8 BioMed Research International
Supplementary Materials
Table S1 differentially expressed circRNAs between tumorandnormal tissues Table S2 differentially expressedmiRNAsbetween tumor and normal tissues (Supplementary Materi-als)
References
[1] L A Torre F Bray R L Siegel J Ferlay and J Lortet-Tieu-lent ldquoGlobal cancer statistics 2012rdquo CA A Cancer Journal forClinicians vol 65 no 2 pp 87ndash108 2015
[2] J Park do N J Thomas C Yoon and S S Yoon ldquoVascularendothelial growth factor a inhibition in gastric cancerrdquoGastricCancer vol 18 no 1 pp 33ndash42 2015
[3] P Karimi F Islami S Anandasabapathy N D Freedman andF Kamangar ldquoGastric cancer descriptive epidemiology riskfactors screening and preventionrdquo Cancer Epidemiology Bio-markers amp Prevention vol 23 no 5 pp 700ndash713 2014
[4] G Spolverato A Ejaz Y Kim et al ldquoRates and patterns of re-currence after curative intent resection for gastric cancerA United States multi-institutional analysisrdquo Journal of theAmerican College of Surgeons vol 219 no 4 pp 664ndash675 2014
[5] X Qi Y Liu W Wang et al ldquoManagement of advanced gastriccancer An overview of major findings from meta-analysisrdquoOncotarget vol 7 no 47 pp 78180ndash78205 2016
[6] J E Wilusz and P A Sharp ldquoA circuitous route to noncodingRNArdquo Science vol 340 no 6131 pp 440-441 2013
[7] W R Jeck J A Sorrentino K Wang et al ldquoCircular RNAs areabundant conserved and associated with ALU repeatsrdquo RNAvol 19 no 2 pp 141ndash157 2013
[8] S Memczak M Jens A Elefsinioti et al ldquoCircular RNAs area large class of animal RNAs with regulatory potencyrdquo Naturevol 495 no 7441 pp 333ndash338 2013
[9] Y-G Zhang H-L Yang Y Long and W-L Li ldquoCircularRNA in blood corpuscles combined with plasma protein factorfor early prediction of pre-eclampsiardquo BJOG An InternationalJournal of Obstetrics amp Gynaecology vol 123 no 13 pp 2113ndash2118 2016
[10] Y Li Q Zheng C Bao et al ldquoCircular RNA is enriched andstable in exosomes a promising biomarker for cancer diagno-sisrdquo Cell Research vol 25 no 8 pp 981ndash984 2015
[11] J Guarnerio M Bezzi J C Jeong et al ldquoOncogenic Role ofFusion-circRNAs Derived from Cancer-Associated Chromoso-mal Translocationsrdquo Cell vol 165 pp 289ndash302 2016
[12] X Wang Y Zhang L Huang et al ldquoDecreased expressionof hsa circ 001988 in colorectal cancer and its clinical sig-nificancesrdquo International Journal of Clinical and ExperimentalPathology vol 8 no 12 pp 16020ndash16025 2015
[13] MQin G Liu X Huo et al ldquoHsa circ 0001649 a circular RNAand potential novel biomarker for hepatocellular carcinomardquoCancer Biomarkers vol 16 no 1 pp 161ndash169 2016
[14] L Xuan L Qu H Zhou et al ldquoCircular RNA a novel bio-marker for progressive laryngeal cancerrdquo American Journal ofTranslational Research vol 8 no 2 pp 932ndash939 2016
[15] X Song N Zhang P Han et al ldquoCircular RNA profile in glio-mas revealed by identification tool UROBORUSrdquoNucleic AcidsResearch vol 44 no 9 article no e87 2016
[16] H L Sanger G Klotz D Riesner H J Gross and A KKleinschmidt ldquoViroids are single stranded covalently closedcircular RNA molecules existing as highly base paired rod like
structuresrdquo Proceedings of the National Acadamy of Sciences ofthe United States of America vol 73 no 11 pp 3852ndash3856 1976
[17] WWDu L FangW Yang et al ldquoInduction of tumor apoptosisthrough a circular RNA enhancing Foxo3 activityrdquoCell DeathampDifferentiation vol 24 no 2 pp 357ndash370 2017
[18] M Armakola M J Higgins M D Figley et al ldquoInhibition ofRNA lariat debranching enzyme suppresses TDP-43 toxicity inALS disease modelsrdquo Nature Genetics vol 44 no 12 pp 1302ndash1309 2012
[19] Z Li C Huang C Bao et al ldquoExon-intron circular RNAs regu-late transcription in the nucleusrdquoNature Structural ampMolecularBiology vol 22 no 3 pp 256ndash264 2015
[20] R Ashwal-Fluss M Meyer N R Pamudurti et al ldquoCircRNAbiogenesis competes with pre-mRNA splicingrdquo Molecular Cellvol 56 no 1 pp 55ndash66 2014
[21] WW Du W Yang E Liu Z Yang P Dhaliwal and B B YangldquoFoxo3 circular RNA retards cell cycle progression via formingternary complexes with p21 and CDK2rdquoNucleic Acids Researchvol 44 no 6 pp 2846ndash2858 2016
[22] A J Enright B John U Gaul T Tuschl C Sander and D SMarks ldquoMicroRNA targets inDrosophilardquoGenomeBiology vol5 no 1 2003
[23] A E Pasquinelli ldquoMicroRNAs and their targets recognitionregulation and an emerging reciprocal relationshiprdquo NatureReviews Genetics vol 13 no 4 pp 271ndash282 2012
[24] I S Vlachos M D Paraskevopoulou D Karagkouni et al ldquoDI-ANA-TarBase v70 indexing more than half a million experi-mentally supported miRNAmRNA interactionsrdquoNucleic AcidsResearch vol 43 no 1 pp D153ndashD159 2015
[25] G Bindea B Mlecnik H Hackl et al ldquoClueGO a Cytoscapeplug-in to decipher functionally grouped gene ontology andpathway annotation networksrdquoBioinformatics vol 25 no 8 pp1091ndash1093 2009
[26] G Bindea J Galon and B Mlecnik ldquoCluePedia Cytoscapeplugin pathway insights using integrated experimental and insilico datardquo Bioinformatics vol 29 no 5 pp 661ndash663 2013
[27] G Scardoni M Petterlini and C Laudanna ldquoAnalyzing biolog-ical network parameters with CentiScaPerdquo Bioinformatics vol25 no 21 pp 2857ndash2859 2009
[28] M B Assumpcao F C Moreira I G Hamoy et al ldquoHigh-throughput miRNA sequencing reveals a field effect in gastriccancer and suggests an epigenetic network mechanismrdquo Bioin-formatics and Biology Insights vol 9 pp 111ndash117 2015
[29] O Yang J Huang and S Lin ldquoRegulatory effects of miRNA ongastric cancer cellsrdquo Oncology Letters vol 8 no 2 pp 651ndash6562014
[30] S Meng H Zhou Z Feng et al ldquoCircRNA Functions andproperties of a novel potential biomarker for cancerrdquoMolecularCancer vol 16 no 1 article no 94 2017
[31] L Chen S Zhang J Wu et al ldquoCircRNA-100290 plays a role inoral cancer by functioning as a sponge of the MIR-29 familyrdquoOncogene vol 36 no 32 pp 4551ndash4561 2017
[32] A Saxena S K Shukla K N Prasad and U C Ghoshal ldquoAnal-ysis of p53 K-ras gene mutation Helicobacter pylori infectionin patients with gastric cancer peptic ulcer disease at a tertiarycare hospital in north Indiardquo Indian Journal ofMedical Researchvol 136 pp 664ndash670 2012
[33] S Uchino M Noguchi A Ochiai T Saito M Kobayashi andS Hirohashi ldquop53 mutation in gastric cancer a genetic modelfor carcinogenesis is common to gastric and colorectal cancerrdquoInternational Journal of Cancer vol 54 no 5 pp 759ndash764 1993
BioMed Research International 9
[34] J He G Zhu L Gao et al ldquoFra-1 is upregulated in gastric cancertissues and affects the PI3KAkt and p53 signaling pathway ingastric cancerrdquo International Journal of Oncology vol 47 no 5pp 1725ndash1734 2015
[35] T Zheng JWang H Jiang and L Liu ldquoHippo signaling in ovalcells and hepatocarcinogenesisrdquo Cancer Letters vol 302 no 2pp 91ndash99 2011
[36] L Zhang X Liu H Jin et al ldquoMiR-206 inhibits gastric cancerproliferation in part by repressing CyclinD2rdquo Cancer Lettersvol 332 no 1 pp 94ndash101 2013
[37] H Shi J Han S Yue T Zhang W Zhu and D Zhang ldquoProg-nostic significance of combined microRNA-206 and CyclinD2in gastric cancer patients after curative surgery A retrospectivecohort studyrdquo Biomedicine amp Pharmacotherapy vol 71 pp 210ndash215 2015
[38] D W Thomson and M E Dinger ldquoEndogenous microRNAsponges evidence and controversyrdquo Nature Reviews Geneticsvol 17 no 5 pp 272ndash283 2016
[39] L Salmena L Poliseno Y Tay L Kats and P P Pandolfi ldquoAceRNA hypothesis the rosetta stone of a hidden RNA lan-guagerdquo Cell vol 146 no 3 pp 353ndash358 2011
[40] X Bao T Ren Y Huang et al ldquoKnockdown of long non-codingRNA HOTAIR increases miR-454-3p by targeting Stat3 andAtg12 to inhibit chondrosarcoma growthrdquoCell DeathampDiseasevol 8 no 2 pp e2605ndashe2605 2017
[41] N Shao L Wang L Xue R Wang and Q Lan ldquoPlasma miR-454-3p as a potential prognostic indicator in human gliomardquoNeurological Sciences vol 36 no 2 pp 309ndash313 2015
Stem Cells International
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
MEDIATORSINFLAMMATION
of
EndocrinologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Disease Markers
Hindawiwwwhindawicom Volume 2018
BioMed Research International
OncologyJournal of
Hindawiwwwhindawicom Volume 2013
Hindawiwwwhindawicom Volume 2018
Oxidative Medicine and Cellular Longevity
Hindawiwwwhindawicom Volume 2018
PPAR Research
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018
Immunology ResearchHindawiwwwhindawicom Volume 2018
Journal of
ObesityJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Computational and Mathematical Methods in Medicine
Hindawiwwwhindawicom Volume 2018
Behavioural Neurology
OphthalmologyJournal of
Hindawiwwwhindawicom Volume 2018
Diabetes ResearchJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Research and TreatmentAIDS
Hindawiwwwhindawicom Volume 2018
Gastroenterology Research and Practice
Hindawiwwwhindawicom Volume 2018
Parkinsonrsquos Disease
Evidence-Based Complementary andAlternative Medicine
Volume 2018Hindawiwwwhindawicom
Submit your manuscripts atwwwhindawicom
6 BioMed Research International
0 1 2 3 4 5 6Anteriorposterior pattern specification
Liver developmentTranscription DNA-templated
Positive regulation of axon extensionNegative regulation of transcription from RNA polymerase II promoter
Regulation of cell adhesionMuscle cell differentiation
G1S transition of mitotic cell cycleRegulation of mRNA stability
Negative regulation of myeloid cell differentiationCytoplasmic stress granule
CytosolNucleoplasm
NucleusSpindle poleCentrosome
Cytoplasmic mRNA processing bodyMembrane
Microtubule associated complexCytoplasm
Protein bindingProtein kinase binding
Sequence-specific DNA bindingPoly(A) RNA binding
Transcriptional repressor activity RNA polymerase II core promoter proximal region sequence-specific bindingRNA binding
Kinase activityProtein phosphatase binding
Nuclear localization sequence bindingPhosphoprotein binding
minuslog(P value)
Figure 4The top 10 enrichment scores in gene ontology (GO) enrichment analysis on target genes of selectedmiRNAs Green bars representcell component terms Blue bars represent molecular function terms
0 05 1 15 2 25 3 35 4 45
Cell cycle
Oocyte meiosis
p53 signaling pathway
Axon guidance
Measles
MicroRNAs in cancer
Hepatitis B
Prostate cancer
Hippo signaling pathway
Neurotrophin signaling pathway
minuslog(P value)
(a)
PPP2CA
CCND2
CALM3
Oocyte meiosise
ZMAT3
ITPR3
p53 signaling pathway
CDC27
CDC25B
SESN3
WEE1
E2F3
CCNE1
CDKN1B
Cell cyclecyl
SEMA4C
Axon guidancei
LATS2
EPHA7
GSK3BPATJPARD6B
SLIT2
ROCK2
Hippo signaling nalipathway
(b)
Figure 5 The KEGG pathway enrichment analysis on target genes of the selected miRNAs (a) The top 10 enrichment scores in the KEGGpathway analysis of the target genes are shown (b) The network composed of the most enriched pathways and their related genes is shown
function in gastric cancer by bioinformatics analysis We willgather more clinical samples and validate our findings infuture work
Conflicts of Interest
The authors declare that there are no conflicts of interest
Authorsrsquo Contributions
Wei Gu and Ying Sun are equal contributors to this work
Acknowledgments
The authors thank CloudSeq Inc for the bioinformatic sup-port
BioMed Research International 7
Table 1 The list of differentially expressed genes involved in the PPI network (betweenness gt 4000)
Gene name Betweenness Degree Stress Closenesshsa-miR-27a-3p 1319550 24 46060 00015hsa-miR-15a-5p 1203950 19 30488 00015NUFIP2 725859 2 1440 00015hsa-miR-148a-3p 701686 18 67106 00013hsa-miR-17-5p 634249 13 156 00014BTG2 611600 2 160 00013hsa-miR-21-5p 606000 7 42 00011DCP2 560967 2 5090 00014ARAP2 538724 4 4698 00013hsa-miR-301a-3p 516847 22 52418 00013hsa circRNA 104682 438000 2 16344 00013YOD1 438000 2 160 00010hsa-miR-196a-5p 429800 15 14042 00009CCND2 428022 2 1440 00014hsa-miR-652-5p 416600 4 7722 00011
hsa_circRNA_104575
hsa-miR-145-5p
CCNJ
hsa-miR-181b-5p
BIRC6
ANGPT2
hsa_circRNA_101222
PKD2
DYNC1LI2
SQSTM1
hsa_circRNA_104697hsa-miR-28-5p
FOXJ3
hsa-miR-193a-3p
PUM1
CDKN1B
hsa_circRNA_104168 AGPAT3
PTPRG
HOXC8
hsa-miR-196a-5p
HAND1
PSMD11
hsa_circRNA_105049
IGF2BP1
PRTG
HOXA5
KHSRP
GSK3BHOXB8
MSI2
ACLY
SLC9A6
GATAD2B
HOXA9
hsa_circRNA_103552
hsa-miR-21-5p
YOD1
BTG2
CCDC126EPHA7GATA6
HOXB6
KLHL15 FRS2 PARD6B
ADNP
CUL5
HBP1
hsa_circRNA_104634
hsa-miR-145-3p
CDC25Bhsa_circRNA_100383
KIF1BRAB14
QKI
MTPN
hsa_circRNA_103349
hsa-miR-214-3phsa_circRNA_102064
hsa_circRNA_101858
CNIH1
GALNT7
hsa_circRNA_101017
ASB1
ITPR3
BHLHE41
ADAMTS5
hsa-miR-148a-3p
NPTX1 KIAA0226
NRARP
SPIRE1
ARL6IP1 FAM178A
hsa_circRNA_101504
PGM2L1
HPRT1
hsa-miR-301a-3p
B4GALT5
CNOT4
MLLT10
PPP6R1
SESTD1ATP11A
C7orf60
hsa-miR-17-5p
ROCK2
hsa-miR-93-5p
CAMTA1
EFCAB14
hsa_circRNA_104651
RGMB
ZNF148
PSAP
RRAGDPTPN4
ZMAT3hsa_circRNA_103840
TSHZ1
STEAP4ANKRD52
TBCEL
NPAT
ARAP2
BRWD1
hsa-miR-454-3p
SMARCD2
LDLR
PPP2CAhsa-miR-652-5p
RC3H2
hsa_circRNA_104682
MAP1B
hsa_circRNA_100583
LATS2
CEP85L
hsa-miR-135b-5p
SLC6A5
CENPB
MAN2A1
ZBTB18 ZNF800
LONP2LONRF2
ABL2 PEG10
PRDM4
SLIT2
DLL1
CBX4
hsa-miR-15a-5pTMEM245
CCNE1
DDX3XWEE1
CHAC1
ARL2
PAFAH1B1RB1CC1
ATP13A3
DCP2
NABP1
SPTY2D1
hsa-miR-27a-3p
SESN3
CDK16
RORA
JARID2
CHAMP1
NUFIP2
FAM118A
MFHAS1
SEMA4C
KPNA6
hsa_circRNA_104533
hsa-miR-125b-5pZC3H7B
hsa_circRNA_100319
KPNB1
hsa_circRNA_102700
TNPO1
hsa-miR-27b-3p
CALM3
RASL11B
hsa-miR-20b-5p
hsa_circRNA_105039
PFN2
TBC1D9TMEM167A
hsa-miR-18b-5p
hsa_circRNA_102062
INADL
TAOK1
GIGYF1
CAMK2N1
CADhsa-miR-18a-5p
RNF187
E2F3
CACUL1
CDC27ZNF367
SPRTN
CCND2
ARNTL2STAT3
MAP3K9
SUV420H1
ANKRD13C
PRR15
hsa-miR-660-3p
APH1A
hsa_circRNA_104374FAM98A
hsa_circRNA_100013
NOP9
mRNAmiRNAcircRNA
minus85 85
Fold change
0
Figure 6 The visualization of the circRNA-miRNA-mRNA regulation network The circular blue nodes represent mRNAs the diamondnodes represent the miRNAs and round rectangle nodes represent the circRNAs ldquoRedrdquo indicates high relative expression and ldquogreenrdquoindicates low relative expression
8 BioMed Research International
Supplementary Materials
Table S1 differentially expressed circRNAs between tumorandnormal tissues Table S2 differentially expressedmiRNAsbetween tumor and normal tissues (Supplementary Materi-als)
References
[1] L A Torre F Bray R L Siegel J Ferlay and J Lortet-Tieu-lent ldquoGlobal cancer statistics 2012rdquo CA A Cancer Journal forClinicians vol 65 no 2 pp 87ndash108 2015
[2] J Park do N J Thomas C Yoon and S S Yoon ldquoVascularendothelial growth factor a inhibition in gastric cancerrdquoGastricCancer vol 18 no 1 pp 33ndash42 2015
[3] P Karimi F Islami S Anandasabapathy N D Freedman andF Kamangar ldquoGastric cancer descriptive epidemiology riskfactors screening and preventionrdquo Cancer Epidemiology Bio-markers amp Prevention vol 23 no 5 pp 700ndash713 2014
[4] G Spolverato A Ejaz Y Kim et al ldquoRates and patterns of re-currence after curative intent resection for gastric cancerA United States multi-institutional analysisrdquo Journal of theAmerican College of Surgeons vol 219 no 4 pp 664ndash675 2014
[5] X Qi Y Liu W Wang et al ldquoManagement of advanced gastriccancer An overview of major findings from meta-analysisrdquoOncotarget vol 7 no 47 pp 78180ndash78205 2016
[6] J E Wilusz and P A Sharp ldquoA circuitous route to noncodingRNArdquo Science vol 340 no 6131 pp 440-441 2013
[7] W R Jeck J A Sorrentino K Wang et al ldquoCircular RNAs areabundant conserved and associated with ALU repeatsrdquo RNAvol 19 no 2 pp 141ndash157 2013
[8] S Memczak M Jens A Elefsinioti et al ldquoCircular RNAs area large class of animal RNAs with regulatory potencyrdquo Naturevol 495 no 7441 pp 333ndash338 2013
[9] Y-G Zhang H-L Yang Y Long and W-L Li ldquoCircularRNA in blood corpuscles combined with plasma protein factorfor early prediction of pre-eclampsiardquo BJOG An InternationalJournal of Obstetrics amp Gynaecology vol 123 no 13 pp 2113ndash2118 2016
[10] Y Li Q Zheng C Bao et al ldquoCircular RNA is enriched andstable in exosomes a promising biomarker for cancer diagno-sisrdquo Cell Research vol 25 no 8 pp 981ndash984 2015
[11] J Guarnerio M Bezzi J C Jeong et al ldquoOncogenic Role ofFusion-circRNAs Derived from Cancer-Associated Chromoso-mal Translocationsrdquo Cell vol 165 pp 289ndash302 2016
[12] X Wang Y Zhang L Huang et al ldquoDecreased expressionof hsa circ 001988 in colorectal cancer and its clinical sig-nificancesrdquo International Journal of Clinical and ExperimentalPathology vol 8 no 12 pp 16020ndash16025 2015
[13] MQin G Liu X Huo et al ldquoHsa circ 0001649 a circular RNAand potential novel biomarker for hepatocellular carcinomardquoCancer Biomarkers vol 16 no 1 pp 161ndash169 2016
[14] L Xuan L Qu H Zhou et al ldquoCircular RNA a novel bio-marker for progressive laryngeal cancerrdquo American Journal ofTranslational Research vol 8 no 2 pp 932ndash939 2016
[15] X Song N Zhang P Han et al ldquoCircular RNA profile in glio-mas revealed by identification tool UROBORUSrdquoNucleic AcidsResearch vol 44 no 9 article no e87 2016
[16] H L Sanger G Klotz D Riesner H J Gross and A KKleinschmidt ldquoViroids are single stranded covalently closedcircular RNA molecules existing as highly base paired rod like
structuresrdquo Proceedings of the National Acadamy of Sciences ofthe United States of America vol 73 no 11 pp 3852ndash3856 1976
[17] WWDu L FangW Yang et al ldquoInduction of tumor apoptosisthrough a circular RNA enhancing Foxo3 activityrdquoCell DeathampDifferentiation vol 24 no 2 pp 357ndash370 2017
[18] M Armakola M J Higgins M D Figley et al ldquoInhibition ofRNA lariat debranching enzyme suppresses TDP-43 toxicity inALS disease modelsrdquo Nature Genetics vol 44 no 12 pp 1302ndash1309 2012
[19] Z Li C Huang C Bao et al ldquoExon-intron circular RNAs regu-late transcription in the nucleusrdquoNature Structural ampMolecularBiology vol 22 no 3 pp 256ndash264 2015
[20] R Ashwal-Fluss M Meyer N R Pamudurti et al ldquoCircRNAbiogenesis competes with pre-mRNA splicingrdquo Molecular Cellvol 56 no 1 pp 55ndash66 2014
[21] WW Du W Yang E Liu Z Yang P Dhaliwal and B B YangldquoFoxo3 circular RNA retards cell cycle progression via formingternary complexes with p21 and CDK2rdquoNucleic Acids Researchvol 44 no 6 pp 2846ndash2858 2016
[22] A J Enright B John U Gaul T Tuschl C Sander and D SMarks ldquoMicroRNA targets inDrosophilardquoGenomeBiology vol5 no 1 2003
[23] A E Pasquinelli ldquoMicroRNAs and their targets recognitionregulation and an emerging reciprocal relationshiprdquo NatureReviews Genetics vol 13 no 4 pp 271ndash282 2012
[24] I S Vlachos M D Paraskevopoulou D Karagkouni et al ldquoDI-ANA-TarBase v70 indexing more than half a million experi-mentally supported miRNAmRNA interactionsrdquoNucleic AcidsResearch vol 43 no 1 pp D153ndashD159 2015
[25] G Bindea B Mlecnik H Hackl et al ldquoClueGO a Cytoscapeplug-in to decipher functionally grouped gene ontology andpathway annotation networksrdquoBioinformatics vol 25 no 8 pp1091ndash1093 2009
[26] G Bindea J Galon and B Mlecnik ldquoCluePedia Cytoscapeplugin pathway insights using integrated experimental and insilico datardquo Bioinformatics vol 29 no 5 pp 661ndash663 2013
[27] G Scardoni M Petterlini and C Laudanna ldquoAnalyzing biolog-ical network parameters with CentiScaPerdquo Bioinformatics vol25 no 21 pp 2857ndash2859 2009
[28] M B Assumpcao F C Moreira I G Hamoy et al ldquoHigh-throughput miRNA sequencing reveals a field effect in gastriccancer and suggests an epigenetic network mechanismrdquo Bioin-formatics and Biology Insights vol 9 pp 111ndash117 2015
[29] O Yang J Huang and S Lin ldquoRegulatory effects of miRNA ongastric cancer cellsrdquo Oncology Letters vol 8 no 2 pp 651ndash6562014
[30] S Meng H Zhou Z Feng et al ldquoCircRNA Functions andproperties of a novel potential biomarker for cancerrdquoMolecularCancer vol 16 no 1 article no 94 2017
[31] L Chen S Zhang J Wu et al ldquoCircRNA-100290 plays a role inoral cancer by functioning as a sponge of the MIR-29 familyrdquoOncogene vol 36 no 32 pp 4551ndash4561 2017
[32] A Saxena S K Shukla K N Prasad and U C Ghoshal ldquoAnal-ysis of p53 K-ras gene mutation Helicobacter pylori infectionin patients with gastric cancer peptic ulcer disease at a tertiarycare hospital in north Indiardquo Indian Journal ofMedical Researchvol 136 pp 664ndash670 2012
[33] S Uchino M Noguchi A Ochiai T Saito M Kobayashi andS Hirohashi ldquop53 mutation in gastric cancer a genetic modelfor carcinogenesis is common to gastric and colorectal cancerrdquoInternational Journal of Cancer vol 54 no 5 pp 759ndash764 1993
BioMed Research International 9
[34] J He G Zhu L Gao et al ldquoFra-1 is upregulated in gastric cancertissues and affects the PI3KAkt and p53 signaling pathway ingastric cancerrdquo International Journal of Oncology vol 47 no 5pp 1725ndash1734 2015
[35] T Zheng JWang H Jiang and L Liu ldquoHippo signaling in ovalcells and hepatocarcinogenesisrdquo Cancer Letters vol 302 no 2pp 91ndash99 2011
[36] L Zhang X Liu H Jin et al ldquoMiR-206 inhibits gastric cancerproliferation in part by repressing CyclinD2rdquo Cancer Lettersvol 332 no 1 pp 94ndash101 2013
[37] H Shi J Han S Yue T Zhang W Zhu and D Zhang ldquoProg-nostic significance of combined microRNA-206 and CyclinD2in gastric cancer patients after curative surgery A retrospectivecohort studyrdquo Biomedicine amp Pharmacotherapy vol 71 pp 210ndash215 2015
[38] D W Thomson and M E Dinger ldquoEndogenous microRNAsponges evidence and controversyrdquo Nature Reviews Geneticsvol 17 no 5 pp 272ndash283 2016
[39] L Salmena L Poliseno Y Tay L Kats and P P Pandolfi ldquoAceRNA hypothesis the rosetta stone of a hidden RNA lan-guagerdquo Cell vol 146 no 3 pp 353ndash358 2011
[40] X Bao T Ren Y Huang et al ldquoKnockdown of long non-codingRNA HOTAIR increases miR-454-3p by targeting Stat3 andAtg12 to inhibit chondrosarcoma growthrdquoCell DeathampDiseasevol 8 no 2 pp e2605ndashe2605 2017
[41] N Shao L Wang L Xue R Wang and Q Lan ldquoPlasma miR-454-3p as a potential prognostic indicator in human gliomardquoNeurological Sciences vol 36 no 2 pp 309ndash313 2015
Stem Cells International
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
MEDIATORSINFLAMMATION
of
EndocrinologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Disease Markers
Hindawiwwwhindawicom Volume 2018
BioMed Research International
OncologyJournal of
Hindawiwwwhindawicom Volume 2013
Hindawiwwwhindawicom Volume 2018
Oxidative Medicine and Cellular Longevity
Hindawiwwwhindawicom Volume 2018
PPAR Research
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018
Immunology ResearchHindawiwwwhindawicom Volume 2018
Journal of
ObesityJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Computational and Mathematical Methods in Medicine
Hindawiwwwhindawicom Volume 2018
Behavioural Neurology
OphthalmologyJournal of
Hindawiwwwhindawicom Volume 2018
Diabetes ResearchJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Research and TreatmentAIDS
Hindawiwwwhindawicom Volume 2018
Gastroenterology Research and Practice
Hindawiwwwhindawicom Volume 2018
Parkinsonrsquos Disease
Evidence-Based Complementary andAlternative Medicine
Volume 2018Hindawiwwwhindawicom
Submit your manuscripts atwwwhindawicom
BioMed Research International 7
Table 1 The list of differentially expressed genes involved in the PPI network (betweenness gt 4000)
Gene name Betweenness Degree Stress Closenesshsa-miR-27a-3p 1319550 24 46060 00015hsa-miR-15a-5p 1203950 19 30488 00015NUFIP2 725859 2 1440 00015hsa-miR-148a-3p 701686 18 67106 00013hsa-miR-17-5p 634249 13 156 00014BTG2 611600 2 160 00013hsa-miR-21-5p 606000 7 42 00011DCP2 560967 2 5090 00014ARAP2 538724 4 4698 00013hsa-miR-301a-3p 516847 22 52418 00013hsa circRNA 104682 438000 2 16344 00013YOD1 438000 2 160 00010hsa-miR-196a-5p 429800 15 14042 00009CCND2 428022 2 1440 00014hsa-miR-652-5p 416600 4 7722 00011
hsa_circRNA_104575
hsa-miR-145-5p
CCNJ
hsa-miR-181b-5p
BIRC6
ANGPT2
hsa_circRNA_101222
PKD2
DYNC1LI2
SQSTM1
hsa_circRNA_104697hsa-miR-28-5p
FOXJ3
hsa-miR-193a-3p
PUM1
CDKN1B
hsa_circRNA_104168 AGPAT3
PTPRG
HOXC8
hsa-miR-196a-5p
HAND1
PSMD11
hsa_circRNA_105049
IGF2BP1
PRTG
HOXA5
KHSRP
GSK3BHOXB8
MSI2
ACLY
SLC9A6
GATAD2B
HOXA9
hsa_circRNA_103552
hsa-miR-21-5p
YOD1
BTG2
CCDC126EPHA7GATA6
HOXB6
KLHL15 FRS2 PARD6B
ADNP
CUL5
HBP1
hsa_circRNA_104634
hsa-miR-145-3p
CDC25Bhsa_circRNA_100383
KIF1BRAB14
QKI
MTPN
hsa_circRNA_103349
hsa-miR-214-3phsa_circRNA_102064
hsa_circRNA_101858
CNIH1
GALNT7
hsa_circRNA_101017
ASB1
ITPR3
BHLHE41
ADAMTS5
hsa-miR-148a-3p
NPTX1 KIAA0226
NRARP
SPIRE1
ARL6IP1 FAM178A
hsa_circRNA_101504
PGM2L1
HPRT1
hsa-miR-301a-3p
B4GALT5
CNOT4
MLLT10
PPP6R1
SESTD1ATP11A
C7orf60
hsa-miR-17-5p
ROCK2
hsa-miR-93-5p
CAMTA1
EFCAB14
hsa_circRNA_104651
RGMB
ZNF148
PSAP
RRAGDPTPN4
ZMAT3hsa_circRNA_103840
TSHZ1
STEAP4ANKRD52
TBCEL
NPAT
ARAP2
BRWD1
hsa-miR-454-3p
SMARCD2
LDLR
PPP2CAhsa-miR-652-5p
RC3H2
hsa_circRNA_104682
MAP1B
hsa_circRNA_100583
LATS2
CEP85L
hsa-miR-135b-5p
SLC6A5
CENPB
MAN2A1
ZBTB18 ZNF800
LONP2LONRF2
ABL2 PEG10
PRDM4
SLIT2
DLL1
CBX4
hsa-miR-15a-5pTMEM245
CCNE1
DDX3XWEE1
CHAC1
ARL2
PAFAH1B1RB1CC1
ATP13A3
DCP2
NABP1
SPTY2D1
hsa-miR-27a-3p
SESN3
CDK16
RORA
JARID2
CHAMP1
NUFIP2
FAM118A
MFHAS1
SEMA4C
KPNA6
hsa_circRNA_104533
hsa-miR-125b-5pZC3H7B
hsa_circRNA_100319
KPNB1
hsa_circRNA_102700
TNPO1
hsa-miR-27b-3p
CALM3
RASL11B
hsa-miR-20b-5p
hsa_circRNA_105039
PFN2
TBC1D9TMEM167A
hsa-miR-18b-5p
hsa_circRNA_102062
INADL
TAOK1
GIGYF1
CAMK2N1
CADhsa-miR-18a-5p
RNF187
E2F3
CACUL1
CDC27ZNF367
SPRTN
CCND2
ARNTL2STAT3
MAP3K9
SUV420H1
ANKRD13C
PRR15
hsa-miR-660-3p
APH1A
hsa_circRNA_104374FAM98A
hsa_circRNA_100013
NOP9
mRNAmiRNAcircRNA
minus85 85
Fold change
0
Figure 6 The visualization of the circRNA-miRNA-mRNA regulation network The circular blue nodes represent mRNAs the diamondnodes represent the miRNAs and round rectangle nodes represent the circRNAs ldquoRedrdquo indicates high relative expression and ldquogreenrdquoindicates low relative expression
8 BioMed Research International
Supplementary Materials
Table S1 differentially expressed circRNAs between tumorandnormal tissues Table S2 differentially expressedmiRNAsbetween tumor and normal tissues (Supplementary Materi-als)
References
[1] L A Torre F Bray R L Siegel J Ferlay and J Lortet-Tieu-lent ldquoGlobal cancer statistics 2012rdquo CA A Cancer Journal forClinicians vol 65 no 2 pp 87ndash108 2015
[2] J Park do N J Thomas C Yoon and S S Yoon ldquoVascularendothelial growth factor a inhibition in gastric cancerrdquoGastricCancer vol 18 no 1 pp 33ndash42 2015
[3] P Karimi F Islami S Anandasabapathy N D Freedman andF Kamangar ldquoGastric cancer descriptive epidemiology riskfactors screening and preventionrdquo Cancer Epidemiology Bio-markers amp Prevention vol 23 no 5 pp 700ndash713 2014
[4] G Spolverato A Ejaz Y Kim et al ldquoRates and patterns of re-currence after curative intent resection for gastric cancerA United States multi-institutional analysisrdquo Journal of theAmerican College of Surgeons vol 219 no 4 pp 664ndash675 2014
[5] X Qi Y Liu W Wang et al ldquoManagement of advanced gastriccancer An overview of major findings from meta-analysisrdquoOncotarget vol 7 no 47 pp 78180ndash78205 2016
[6] J E Wilusz and P A Sharp ldquoA circuitous route to noncodingRNArdquo Science vol 340 no 6131 pp 440-441 2013
[7] W R Jeck J A Sorrentino K Wang et al ldquoCircular RNAs areabundant conserved and associated with ALU repeatsrdquo RNAvol 19 no 2 pp 141ndash157 2013
[8] S Memczak M Jens A Elefsinioti et al ldquoCircular RNAs area large class of animal RNAs with regulatory potencyrdquo Naturevol 495 no 7441 pp 333ndash338 2013
[9] Y-G Zhang H-L Yang Y Long and W-L Li ldquoCircularRNA in blood corpuscles combined with plasma protein factorfor early prediction of pre-eclampsiardquo BJOG An InternationalJournal of Obstetrics amp Gynaecology vol 123 no 13 pp 2113ndash2118 2016
[10] Y Li Q Zheng C Bao et al ldquoCircular RNA is enriched andstable in exosomes a promising biomarker for cancer diagno-sisrdquo Cell Research vol 25 no 8 pp 981ndash984 2015
[11] J Guarnerio M Bezzi J C Jeong et al ldquoOncogenic Role ofFusion-circRNAs Derived from Cancer-Associated Chromoso-mal Translocationsrdquo Cell vol 165 pp 289ndash302 2016
[12] X Wang Y Zhang L Huang et al ldquoDecreased expressionof hsa circ 001988 in colorectal cancer and its clinical sig-nificancesrdquo International Journal of Clinical and ExperimentalPathology vol 8 no 12 pp 16020ndash16025 2015
[13] MQin G Liu X Huo et al ldquoHsa circ 0001649 a circular RNAand potential novel biomarker for hepatocellular carcinomardquoCancer Biomarkers vol 16 no 1 pp 161ndash169 2016
[14] L Xuan L Qu H Zhou et al ldquoCircular RNA a novel bio-marker for progressive laryngeal cancerrdquo American Journal ofTranslational Research vol 8 no 2 pp 932ndash939 2016
[15] X Song N Zhang P Han et al ldquoCircular RNA profile in glio-mas revealed by identification tool UROBORUSrdquoNucleic AcidsResearch vol 44 no 9 article no e87 2016
[16] H L Sanger G Klotz D Riesner H J Gross and A KKleinschmidt ldquoViroids are single stranded covalently closedcircular RNA molecules existing as highly base paired rod like
structuresrdquo Proceedings of the National Acadamy of Sciences ofthe United States of America vol 73 no 11 pp 3852ndash3856 1976
[17] WWDu L FangW Yang et al ldquoInduction of tumor apoptosisthrough a circular RNA enhancing Foxo3 activityrdquoCell DeathampDifferentiation vol 24 no 2 pp 357ndash370 2017
[18] M Armakola M J Higgins M D Figley et al ldquoInhibition ofRNA lariat debranching enzyme suppresses TDP-43 toxicity inALS disease modelsrdquo Nature Genetics vol 44 no 12 pp 1302ndash1309 2012
[19] Z Li C Huang C Bao et al ldquoExon-intron circular RNAs regu-late transcription in the nucleusrdquoNature Structural ampMolecularBiology vol 22 no 3 pp 256ndash264 2015
[20] R Ashwal-Fluss M Meyer N R Pamudurti et al ldquoCircRNAbiogenesis competes with pre-mRNA splicingrdquo Molecular Cellvol 56 no 1 pp 55ndash66 2014
[21] WW Du W Yang E Liu Z Yang P Dhaliwal and B B YangldquoFoxo3 circular RNA retards cell cycle progression via formingternary complexes with p21 and CDK2rdquoNucleic Acids Researchvol 44 no 6 pp 2846ndash2858 2016
[22] A J Enright B John U Gaul T Tuschl C Sander and D SMarks ldquoMicroRNA targets inDrosophilardquoGenomeBiology vol5 no 1 2003
[23] A E Pasquinelli ldquoMicroRNAs and their targets recognitionregulation and an emerging reciprocal relationshiprdquo NatureReviews Genetics vol 13 no 4 pp 271ndash282 2012
[24] I S Vlachos M D Paraskevopoulou D Karagkouni et al ldquoDI-ANA-TarBase v70 indexing more than half a million experi-mentally supported miRNAmRNA interactionsrdquoNucleic AcidsResearch vol 43 no 1 pp D153ndashD159 2015
[25] G Bindea B Mlecnik H Hackl et al ldquoClueGO a Cytoscapeplug-in to decipher functionally grouped gene ontology andpathway annotation networksrdquoBioinformatics vol 25 no 8 pp1091ndash1093 2009
[26] G Bindea J Galon and B Mlecnik ldquoCluePedia Cytoscapeplugin pathway insights using integrated experimental and insilico datardquo Bioinformatics vol 29 no 5 pp 661ndash663 2013
[27] G Scardoni M Petterlini and C Laudanna ldquoAnalyzing biolog-ical network parameters with CentiScaPerdquo Bioinformatics vol25 no 21 pp 2857ndash2859 2009
[28] M B Assumpcao F C Moreira I G Hamoy et al ldquoHigh-throughput miRNA sequencing reveals a field effect in gastriccancer and suggests an epigenetic network mechanismrdquo Bioin-formatics and Biology Insights vol 9 pp 111ndash117 2015
[29] O Yang J Huang and S Lin ldquoRegulatory effects of miRNA ongastric cancer cellsrdquo Oncology Letters vol 8 no 2 pp 651ndash6562014
[30] S Meng H Zhou Z Feng et al ldquoCircRNA Functions andproperties of a novel potential biomarker for cancerrdquoMolecularCancer vol 16 no 1 article no 94 2017
[31] L Chen S Zhang J Wu et al ldquoCircRNA-100290 plays a role inoral cancer by functioning as a sponge of the MIR-29 familyrdquoOncogene vol 36 no 32 pp 4551ndash4561 2017
[32] A Saxena S K Shukla K N Prasad and U C Ghoshal ldquoAnal-ysis of p53 K-ras gene mutation Helicobacter pylori infectionin patients with gastric cancer peptic ulcer disease at a tertiarycare hospital in north Indiardquo Indian Journal ofMedical Researchvol 136 pp 664ndash670 2012
[33] S Uchino M Noguchi A Ochiai T Saito M Kobayashi andS Hirohashi ldquop53 mutation in gastric cancer a genetic modelfor carcinogenesis is common to gastric and colorectal cancerrdquoInternational Journal of Cancer vol 54 no 5 pp 759ndash764 1993
BioMed Research International 9
[34] J He G Zhu L Gao et al ldquoFra-1 is upregulated in gastric cancertissues and affects the PI3KAkt and p53 signaling pathway ingastric cancerrdquo International Journal of Oncology vol 47 no 5pp 1725ndash1734 2015
[35] T Zheng JWang H Jiang and L Liu ldquoHippo signaling in ovalcells and hepatocarcinogenesisrdquo Cancer Letters vol 302 no 2pp 91ndash99 2011
[36] L Zhang X Liu H Jin et al ldquoMiR-206 inhibits gastric cancerproliferation in part by repressing CyclinD2rdquo Cancer Lettersvol 332 no 1 pp 94ndash101 2013
[37] H Shi J Han S Yue T Zhang W Zhu and D Zhang ldquoProg-nostic significance of combined microRNA-206 and CyclinD2in gastric cancer patients after curative surgery A retrospectivecohort studyrdquo Biomedicine amp Pharmacotherapy vol 71 pp 210ndash215 2015
[38] D W Thomson and M E Dinger ldquoEndogenous microRNAsponges evidence and controversyrdquo Nature Reviews Geneticsvol 17 no 5 pp 272ndash283 2016
[39] L Salmena L Poliseno Y Tay L Kats and P P Pandolfi ldquoAceRNA hypothesis the rosetta stone of a hidden RNA lan-guagerdquo Cell vol 146 no 3 pp 353ndash358 2011
[40] X Bao T Ren Y Huang et al ldquoKnockdown of long non-codingRNA HOTAIR increases miR-454-3p by targeting Stat3 andAtg12 to inhibit chondrosarcoma growthrdquoCell DeathampDiseasevol 8 no 2 pp e2605ndashe2605 2017
[41] N Shao L Wang L Xue R Wang and Q Lan ldquoPlasma miR-454-3p as a potential prognostic indicator in human gliomardquoNeurological Sciences vol 36 no 2 pp 309ndash313 2015
Stem Cells International
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
MEDIATORSINFLAMMATION
of
EndocrinologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Disease Markers
Hindawiwwwhindawicom Volume 2018
BioMed Research International
OncologyJournal of
Hindawiwwwhindawicom Volume 2013
Hindawiwwwhindawicom Volume 2018
Oxidative Medicine and Cellular Longevity
Hindawiwwwhindawicom Volume 2018
PPAR Research
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018
Immunology ResearchHindawiwwwhindawicom Volume 2018
Journal of
ObesityJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Computational and Mathematical Methods in Medicine
Hindawiwwwhindawicom Volume 2018
Behavioural Neurology
OphthalmologyJournal of
Hindawiwwwhindawicom Volume 2018
Diabetes ResearchJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Research and TreatmentAIDS
Hindawiwwwhindawicom Volume 2018
Gastroenterology Research and Practice
Hindawiwwwhindawicom Volume 2018
Parkinsonrsquos Disease
Evidence-Based Complementary andAlternative Medicine
Volume 2018Hindawiwwwhindawicom
Submit your manuscripts atwwwhindawicom
8 BioMed Research International
Supplementary Materials
Table S1 differentially expressed circRNAs between tumorandnormal tissues Table S2 differentially expressedmiRNAsbetween tumor and normal tissues (Supplementary Materi-als)
References
[1] L A Torre F Bray R L Siegel J Ferlay and J Lortet-Tieu-lent ldquoGlobal cancer statistics 2012rdquo CA A Cancer Journal forClinicians vol 65 no 2 pp 87ndash108 2015
[2] J Park do N J Thomas C Yoon and S S Yoon ldquoVascularendothelial growth factor a inhibition in gastric cancerrdquoGastricCancer vol 18 no 1 pp 33ndash42 2015
[3] P Karimi F Islami S Anandasabapathy N D Freedman andF Kamangar ldquoGastric cancer descriptive epidemiology riskfactors screening and preventionrdquo Cancer Epidemiology Bio-markers amp Prevention vol 23 no 5 pp 700ndash713 2014
[4] G Spolverato A Ejaz Y Kim et al ldquoRates and patterns of re-currence after curative intent resection for gastric cancerA United States multi-institutional analysisrdquo Journal of theAmerican College of Surgeons vol 219 no 4 pp 664ndash675 2014
[5] X Qi Y Liu W Wang et al ldquoManagement of advanced gastriccancer An overview of major findings from meta-analysisrdquoOncotarget vol 7 no 47 pp 78180ndash78205 2016
[6] J E Wilusz and P A Sharp ldquoA circuitous route to noncodingRNArdquo Science vol 340 no 6131 pp 440-441 2013
[7] W R Jeck J A Sorrentino K Wang et al ldquoCircular RNAs areabundant conserved and associated with ALU repeatsrdquo RNAvol 19 no 2 pp 141ndash157 2013
[8] S Memczak M Jens A Elefsinioti et al ldquoCircular RNAs area large class of animal RNAs with regulatory potencyrdquo Naturevol 495 no 7441 pp 333ndash338 2013
[9] Y-G Zhang H-L Yang Y Long and W-L Li ldquoCircularRNA in blood corpuscles combined with plasma protein factorfor early prediction of pre-eclampsiardquo BJOG An InternationalJournal of Obstetrics amp Gynaecology vol 123 no 13 pp 2113ndash2118 2016
[10] Y Li Q Zheng C Bao et al ldquoCircular RNA is enriched andstable in exosomes a promising biomarker for cancer diagno-sisrdquo Cell Research vol 25 no 8 pp 981ndash984 2015
[11] J Guarnerio M Bezzi J C Jeong et al ldquoOncogenic Role ofFusion-circRNAs Derived from Cancer-Associated Chromoso-mal Translocationsrdquo Cell vol 165 pp 289ndash302 2016
[12] X Wang Y Zhang L Huang et al ldquoDecreased expressionof hsa circ 001988 in colorectal cancer and its clinical sig-nificancesrdquo International Journal of Clinical and ExperimentalPathology vol 8 no 12 pp 16020ndash16025 2015
[13] MQin G Liu X Huo et al ldquoHsa circ 0001649 a circular RNAand potential novel biomarker for hepatocellular carcinomardquoCancer Biomarkers vol 16 no 1 pp 161ndash169 2016
[14] L Xuan L Qu H Zhou et al ldquoCircular RNA a novel bio-marker for progressive laryngeal cancerrdquo American Journal ofTranslational Research vol 8 no 2 pp 932ndash939 2016
[15] X Song N Zhang P Han et al ldquoCircular RNA profile in glio-mas revealed by identification tool UROBORUSrdquoNucleic AcidsResearch vol 44 no 9 article no e87 2016
[16] H L Sanger G Klotz D Riesner H J Gross and A KKleinschmidt ldquoViroids are single stranded covalently closedcircular RNA molecules existing as highly base paired rod like
structuresrdquo Proceedings of the National Acadamy of Sciences ofthe United States of America vol 73 no 11 pp 3852ndash3856 1976
[17] WWDu L FangW Yang et al ldquoInduction of tumor apoptosisthrough a circular RNA enhancing Foxo3 activityrdquoCell DeathampDifferentiation vol 24 no 2 pp 357ndash370 2017
[18] M Armakola M J Higgins M D Figley et al ldquoInhibition ofRNA lariat debranching enzyme suppresses TDP-43 toxicity inALS disease modelsrdquo Nature Genetics vol 44 no 12 pp 1302ndash1309 2012
[19] Z Li C Huang C Bao et al ldquoExon-intron circular RNAs regu-late transcription in the nucleusrdquoNature Structural ampMolecularBiology vol 22 no 3 pp 256ndash264 2015
[20] R Ashwal-Fluss M Meyer N R Pamudurti et al ldquoCircRNAbiogenesis competes with pre-mRNA splicingrdquo Molecular Cellvol 56 no 1 pp 55ndash66 2014
[21] WW Du W Yang E Liu Z Yang P Dhaliwal and B B YangldquoFoxo3 circular RNA retards cell cycle progression via formingternary complexes with p21 and CDK2rdquoNucleic Acids Researchvol 44 no 6 pp 2846ndash2858 2016
[22] A J Enright B John U Gaul T Tuschl C Sander and D SMarks ldquoMicroRNA targets inDrosophilardquoGenomeBiology vol5 no 1 2003
[23] A E Pasquinelli ldquoMicroRNAs and their targets recognitionregulation and an emerging reciprocal relationshiprdquo NatureReviews Genetics vol 13 no 4 pp 271ndash282 2012
[24] I S Vlachos M D Paraskevopoulou D Karagkouni et al ldquoDI-ANA-TarBase v70 indexing more than half a million experi-mentally supported miRNAmRNA interactionsrdquoNucleic AcidsResearch vol 43 no 1 pp D153ndashD159 2015
[25] G Bindea B Mlecnik H Hackl et al ldquoClueGO a Cytoscapeplug-in to decipher functionally grouped gene ontology andpathway annotation networksrdquoBioinformatics vol 25 no 8 pp1091ndash1093 2009
[26] G Bindea J Galon and B Mlecnik ldquoCluePedia Cytoscapeplugin pathway insights using integrated experimental and insilico datardquo Bioinformatics vol 29 no 5 pp 661ndash663 2013
[27] G Scardoni M Petterlini and C Laudanna ldquoAnalyzing biolog-ical network parameters with CentiScaPerdquo Bioinformatics vol25 no 21 pp 2857ndash2859 2009
[28] M B Assumpcao F C Moreira I G Hamoy et al ldquoHigh-throughput miRNA sequencing reveals a field effect in gastriccancer and suggests an epigenetic network mechanismrdquo Bioin-formatics and Biology Insights vol 9 pp 111ndash117 2015
[29] O Yang J Huang and S Lin ldquoRegulatory effects of miRNA ongastric cancer cellsrdquo Oncology Letters vol 8 no 2 pp 651ndash6562014
[30] S Meng H Zhou Z Feng et al ldquoCircRNA Functions andproperties of a novel potential biomarker for cancerrdquoMolecularCancer vol 16 no 1 article no 94 2017
[31] L Chen S Zhang J Wu et al ldquoCircRNA-100290 plays a role inoral cancer by functioning as a sponge of the MIR-29 familyrdquoOncogene vol 36 no 32 pp 4551ndash4561 2017
[32] A Saxena S K Shukla K N Prasad and U C Ghoshal ldquoAnal-ysis of p53 K-ras gene mutation Helicobacter pylori infectionin patients with gastric cancer peptic ulcer disease at a tertiarycare hospital in north Indiardquo Indian Journal ofMedical Researchvol 136 pp 664ndash670 2012
[33] S Uchino M Noguchi A Ochiai T Saito M Kobayashi andS Hirohashi ldquop53 mutation in gastric cancer a genetic modelfor carcinogenesis is common to gastric and colorectal cancerrdquoInternational Journal of Cancer vol 54 no 5 pp 759ndash764 1993
BioMed Research International 9
[34] J He G Zhu L Gao et al ldquoFra-1 is upregulated in gastric cancertissues and affects the PI3KAkt and p53 signaling pathway ingastric cancerrdquo International Journal of Oncology vol 47 no 5pp 1725ndash1734 2015
[35] T Zheng JWang H Jiang and L Liu ldquoHippo signaling in ovalcells and hepatocarcinogenesisrdquo Cancer Letters vol 302 no 2pp 91ndash99 2011
[36] L Zhang X Liu H Jin et al ldquoMiR-206 inhibits gastric cancerproliferation in part by repressing CyclinD2rdquo Cancer Lettersvol 332 no 1 pp 94ndash101 2013
[37] H Shi J Han S Yue T Zhang W Zhu and D Zhang ldquoProg-nostic significance of combined microRNA-206 and CyclinD2in gastric cancer patients after curative surgery A retrospectivecohort studyrdquo Biomedicine amp Pharmacotherapy vol 71 pp 210ndash215 2015
[38] D W Thomson and M E Dinger ldquoEndogenous microRNAsponges evidence and controversyrdquo Nature Reviews Geneticsvol 17 no 5 pp 272ndash283 2016
[39] L Salmena L Poliseno Y Tay L Kats and P P Pandolfi ldquoAceRNA hypothesis the rosetta stone of a hidden RNA lan-guagerdquo Cell vol 146 no 3 pp 353ndash358 2011
[40] X Bao T Ren Y Huang et al ldquoKnockdown of long non-codingRNA HOTAIR increases miR-454-3p by targeting Stat3 andAtg12 to inhibit chondrosarcoma growthrdquoCell DeathampDiseasevol 8 no 2 pp e2605ndashe2605 2017
[41] N Shao L Wang L Xue R Wang and Q Lan ldquoPlasma miR-454-3p as a potential prognostic indicator in human gliomardquoNeurological Sciences vol 36 no 2 pp 309ndash313 2015
Stem Cells International
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
MEDIATORSINFLAMMATION
of
EndocrinologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Disease Markers
Hindawiwwwhindawicom Volume 2018
BioMed Research International
OncologyJournal of
Hindawiwwwhindawicom Volume 2013
Hindawiwwwhindawicom Volume 2018
Oxidative Medicine and Cellular Longevity
Hindawiwwwhindawicom Volume 2018
PPAR Research
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018
Immunology ResearchHindawiwwwhindawicom Volume 2018
Journal of
ObesityJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Computational and Mathematical Methods in Medicine
Hindawiwwwhindawicom Volume 2018
Behavioural Neurology
OphthalmologyJournal of
Hindawiwwwhindawicom Volume 2018
Diabetes ResearchJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Research and TreatmentAIDS
Hindawiwwwhindawicom Volume 2018
Gastroenterology Research and Practice
Hindawiwwwhindawicom Volume 2018
Parkinsonrsquos Disease
Evidence-Based Complementary andAlternative Medicine
Volume 2018Hindawiwwwhindawicom
Submit your manuscripts atwwwhindawicom
BioMed Research International 9
[34] J He G Zhu L Gao et al ldquoFra-1 is upregulated in gastric cancertissues and affects the PI3KAkt and p53 signaling pathway ingastric cancerrdquo International Journal of Oncology vol 47 no 5pp 1725ndash1734 2015
[35] T Zheng JWang H Jiang and L Liu ldquoHippo signaling in ovalcells and hepatocarcinogenesisrdquo Cancer Letters vol 302 no 2pp 91ndash99 2011
[36] L Zhang X Liu H Jin et al ldquoMiR-206 inhibits gastric cancerproliferation in part by repressing CyclinD2rdquo Cancer Lettersvol 332 no 1 pp 94ndash101 2013
[37] H Shi J Han S Yue T Zhang W Zhu and D Zhang ldquoProg-nostic significance of combined microRNA-206 and CyclinD2in gastric cancer patients after curative surgery A retrospectivecohort studyrdquo Biomedicine amp Pharmacotherapy vol 71 pp 210ndash215 2015
[38] D W Thomson and M E Dinger ldquoEndogenous microRNAsponges evidence and controversyrdquo Nature Reviews Geneticsvol 17 no 5 pp 272ndash283 2016
[39] L Salmena L Poliseno Y Tay L Kats and P P Pandolfi ldquoAceRNA hypothesis the rosetta stone of a hidden RNA lan-guagerdquo Cell vol 146 no 3 pp 353ndash358 2011
[40] X Bao T Ren Y Huang et al ldquoKnockdown of long non-codingRNA HOTAIR increases miR-454-3p by targeting Stat3 andAtg12 to inhibit chondrosarcoma growthrdquoCell DeathampDiseasevol 8 no 2 pp e2605ndashe2605 2017
[41] N Shao L Wang L Xue R Wang and Q Lan ldquoPlasma miR-454-3p as a potential prognostic indicator in human gliomardquoNeurological Sciences vol 36 no 2 pp 309ndash313 2015
Stem Cells International
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
MEDIATORSINFLAMMATION
of
EndocrinologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Disease Markers
Hindawiwwwhindawicom Volume 2018
BioMed Research International
OncologyJournal of
Hindawiwwwhindawicom Volume 2013
Hindawiwwwhindawicom Volume 2018
Oxidative Medicine and Cellular Longevity
Hindawiwwwhindawicom Volume 2018
PPAR Research
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018
Immunology ResearchHindawiwwwhindawicom Volume 2018
Journal of
ObesityJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Computational and Mathematical Methods in Medicine
Hindawiwwwhindawicom Volume 2018
Behavioural Neurology
OphthalmologyJournal of
Hindawiwwwhindawicom Volume 2018
Diabetes ResearchJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Research and TreatmentAIDS
Hindawiwwwhindawicom Volume 2018
Gastroenterology Research and Practice
Hindawiwwwhindawicom Volume 2018
Parkinsonrsquos Disease
Evidence-Based Complementary andAlternative Medicine
Volume 2018Hindawiwwwhindawicom
Submit your manuscripts atwwwhindawicom
Stem Cells International
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
MEDIATORSINFLAMMATION
of
EndocrinologyInternational Journal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Disease Markers
Hindawiwwwhindawicom Volume 2018
BioMed Research International
OncologyJournal of
Hindawiwwwhindawicom Volume 2013
Hindawiwwwhindawicom Volume 2018
Oxidative Medicine and Cellular Longevity
Hindawiwwwhindawicom Volume 2018
PPAR Research
Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom
The Scientific World Journal
Volume 2018
Immunology ResearchHindawiwwwhindawicom Volume 2018
Journal of
ObesityJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Computational and Mathematical Methods in Medicine
Hindawiwwwhindawicom Volume 2018
Behavioural Neurology
OphthalmologyJournal of
Hindawiwwwhindawicom Volume 2018
Diabetes ResearchJournal of
Hindawiwwwhindawicom Volume 2018
Hindawiwwwhindawicom Volume 2018
Research and TreatmentAIDS
Hindawiwwwhindawicom Volume 2018
Gastroenterology Research and Practice
Hindawiwwwhindawicom Volume 2018
Parkinsonrsquos Disease
Evidence-Based Complementary andAlternative Medicine
Volume 2018Hindawiwwwhindawicom
Submit your manuscripts atwwwhindawicom