Research ArticlePotential Molecular Mechanisms of Chaihu-Shugan-San inTreatment of Breast Cancer Based on Network Pharmacology
Kunmin Xiao,1,2 Kexin Li,1 Sidan Long,1 Chenfan Kong,1 and Shijie Zhu 1,2
1Graduate School, Beijing University of Chinese Medicine, Beijing 100029, China2Department of Oncology, Wangjing Hospital, China Academy of Chinese Medical Sciences, Beijing 100102, China
Correspondence should be addressed to Shijie Zhu; [email protected]
Received 16 May 2020; Accepted 5 August 2020; Published 25 September 2020
Guest Editor: Azis Saifudin
Copyright © 2020 Kunmin Xiao et al./is is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Breast cancer is one of the most common cancers endangering women’s health all over the world. Traditional Chinese medicine isincreasingly recognized as a possible complementary and alternative therapy for breast cancer. Chaihu-Shugan-San is a traditionalChinese medicine prescription, which is extensively used in clinical practice. Its therapeutic effect on breast cancer has attractedextensive attention, but its mechanism of action is still unclear. In this study, we explored the molecular mechanism of Chaihu-Shugan-San in the treatment of breast cancer by network pharmacology. /e results showed that 157 active ingredients and 8074potential drug targets were obtained in the TCMSP database according to the screening conditions. 2384 disease targets werecollected in the TTD, OMIM, DrugBank, GeneCards disease database. We applied the Bisogenet plug-in in Cytoscape 3.7.1 toobtain 451 core targets. /e biological process of gene ontology (GO) involves the mRNA catabolic process, RNA catabolicprocess, telomere organization, nucleobase-containing compound catabolic process, heterocycle catabolic process, and so on. Incellular component, cytosolic part, focal adhesion, cell-substrate adherens junction, and cell-substrate junction are highlycorrelated with breast cancer. In the molecular function category, most proteins were addressed to ubiquitin-like protein ligasebinding, protein domain specific binding, and Nop56p-associated pre-rRNA complex. Besides, the results of the KEGG pathwayanalysis showed that the pathways mainly involved in apoptosis, cell cycle, transcriptional dysregulation, endocrine resistance,and viral infection. In conclusion, the treatment of breast cancer by Chaihu-Shugan-San is the result of multicomponent,multitarget, and multipathway interaction. /is study provides a certain theoretical basis for the treatment of breast cancer byChaihu-Shugan-San and has certain reference value for the development and application of new drugs.
1. Introduction
Breast cancer is one of the most common cancers en-dangering women’s health all over the world. /e GLOBO-CAN 2018 statistics show alarming results that there are 8.6million new cases of female cancer and 4.2 million femalecancer deaths worldwide./e proportion of breast cancer is24.2% and 15.0%, respectively, ranking first in femalecancer incidence and death [1]. It is predicted that, by the2050s, the global incidence of breast cancer will reachnearly 3,200,000 new cases of breast cancer each year./esedatasets reflect the high incidence of breast cancer and theurgent global need for breast cancer prevention andtreatment measures [2].
Traditional Chinese medicine (TCM) has a long historyin the etiology, pathogenesis, prevention, and treatment ofbreast cancer. According to the principle of TCM syndromedifferentiation and treatment, the clinical syndrome ofbreast cancer is mainly “Liver-Qi” stagnation. Chaihu-Shugan-San is one of the classical prescriptions for thetreatment of “Liver-Qi” stagnation. It has the effect ofsoothing “Liver-Qi.” It has a history of 485 years and iswidely used in clinical practice [3–5]. Chaihu-Shugan-Sanincludes seven kinds of traditional Chinese medicine such asBupleurum chinense DC (Chinese name: Chaihu), RadixPaeoniae Alba (Chinese name: Baishao), Citrus reticulataBlanco (Chinese name Chenpi), Cyperus rotundus L(Chinese name: Xiangfu), Glycyrrhiza uralensis Fisch
HindawiEvidence-Based Complementary and Alternative MedicineVolume 2020, Article ID 3670309, 9 pageshttps://doi.org/10.1155/2020/3670309
(Chinese name: Gancao), Citrus aurantium L (Chinesename: Zhiqiao), and Ligusticum chuanxiong Hort (Chinesename: Chuanxiong) [6]. Traditional Chinese medicinemeridian tropism is one of the core components of thetheory of TCM. According to the theory of TCM, thecharacteristics of the selective distribution of the effectivecomponents of traditional Chinese medicine in the body arebasically consistent with the relationship between the visceraand viscera of the corresponding meridian tropism. Chaihu,Chenpi, Xiangfu, Chuanxiong, and Baishao in Chaihu-Shugan-San belong to the liver meridian, which can soothethe liver and regulate Qi well. In traditional Chinesemedicine theory, the main location of breast cancer is in theliver and often related to the spleen and kidney in the processof disease. Chaihu-Shugan-San meridian attribution isconsistent with breast cancer meridian attribution and canbetter play the therapeutic effect.
As a classical prescription, Chaihu-Shugan-San has beenwidely studied in pharmacology. Chaihu-Shugan-San hasthe pharmacological effects of antidepressant [7], regulationof neuro-endocrine-immune network [8, 9], anti-inflammation, antioxidative stress [10], lipid-lowering andglucose-lowering, and antifibrosis [11]. In terms of antitu-mor, in a study of 86 patients with stage III breast cancer, thecontrol group was given a standardized CAF regimen(cyclophosphamide + doxorubicin + 5-fluorouracil), and theexperimental group was treated with Chaihu-Shugan-San onthe basis of CAF. /e short-term effective rate of the ob-servation group was significantly higher than that of thecontrol group (81.4% vs. 58.14%); the Karnofsky improve-ment rate of the observation group was significantly higherthan that of the control group (48.84% vs. 34.88%) [12]. As asafe complementary alternative therapy, Chaihu-Shugan-San combined with other chemotherapy regimens can im-prove the therapeutic effect, alleviate the myelosuppressioncaused by chemotherapeutic drugs, and improve theprognosis of breast cancer patients [13, 14]. However, themechanism of Chaihu-Shugan-San in the treatment ofbreast cancer remains to be further explored.
Classical prescriptions are currently the preferred way to treatdiseases in TCM clinic, but they lack a scientific basis to reasonablyexplain themechanism of TCMprescriptions from the whole to thelocal level or from the cellular to the molecular level [15]. Networkpharmacology is an emerging discipline based on the integration ofsystems biology, molecular biology, pharmacology, and a variety ofnetwork computing platforms in the context of the era of big data,which can more directly explain the association between TCMprescriptions and diseases [16]. /erefore, this study constructed amultidimensional network of “ingredient-target-pathway” throughnetwork pharmacology to explore the potential molecular mecha-nismof Chaihu-Shugan-San in the treatment of breast cancer and toprovide a certain theoretical basis for Chaihu-Shugan-San in thetreatment of breast cancer.
2. Materials and Methods
2.1. Screening of Active Components and Target Prediction inChaihu-Shugan-San. In this study, the chemical compo-nents of the seven herbs were searched on Traditional
Chinese Medicine Systems Pharmacology Database andAnalysis Platform (TCMSP, http://tcmspw.com/tcmsp.php,updated onMay 31, 2014) [17]. Search keywords are Chaihu,Baishao, Chenpi, Xiangfu, Gancao, Zhiqiao, and Chuan-xiong, and only oral bioavailability (OB) ≥30% and drug-likeness (DL) ≥0.18 were considered in this study. /eCanonical SMILES sequence of the compound was searchedin the PubChem database (https://pubchem.ncbi.nlm.nih.gov/) [18], and this sequence was used to predict the target ofthe compound in the Swiss Target Prediction online data-base (http://www.swisstargetprediction.ch/) [19] and collecttarget protein gene names and UniProt ID in the predictionresults.
2.2. Network Construction of Components and Targets./e chemical composition and potential targets of the aboveChaihu-Shugan-San were uploaded to Cytoscape 3.7.1 [20]software to build up the component-target network. In thenetwork, the degree centrality (DC) represents the numberof nodes in the network that directly interacts with the node./e greater the degree, the more the biological functions itparticipates in; the betweenness centrality (BC) refers to theproportion of the number of nodes passing through theshortest path in the network, and the larger the BC is, themore influential the node is. Closeness centrality (CC) re-flects the degree of proximity between nodes, and the re-ciprocal of the shortest path distance from one node to othernodes is CC. /e closer the nodes are, the larger the CC is;the average shortest path length (ASPL) is the average of theshortest path length between all points in the network. /esmaller the average path of a node, the more crucial thisnode is in the network.
2.3. Prediction ofBreastCancerTargets. With “breast cancer”or “malignant breast tumors” as keywords, we searched inOnline Mendelian Inheritance in Man (OMIM, http://www.omim.org/, updated on May 4, 2018) [21], DrugBank(https://www.drugbank.ca/, version 5.1.6, updated on Apr22, 2020) [22],/erapeutic Target Database (TTD, http://db.idrblab.net/ttd/, updated on Nov 11, 2019) [23], and Gen-eCards (https://www.genecards.org/, version 4.14.0) [24] tocollect breast cancer-related targets. In the GeneCards da-tabase, the higher the score value is, the closer the rela-tionship between the target and disease is, and the scorevalue greater than the median is used as the screeningcondition to extract the key target./e above retrieval resultswere combined to remove duplicates and serve as theprediction target library of breast cancer.
2.4. Protein-Protein Interaction (PPI) Network Constructionand Selection of Core Targets. /e BisoGenet plug-in inCytoscape 3.7.1 draws the PPI network and maps theChaihu-Shugan decoction component targets and breastcancer-related disease targets into the protein interactionrelationship network, using Cytoscape 3.7.1 merge twoprotein interaction networks, to extract the intersection ofthe network. Based on the intersection network, the
2 Evidence-Based Complementary and Alternative Medicine
CytoNCA plug-in [25] is used to screen out the nodes whosedegree centrality (DC) is greater than 2 times the median ofall nodes. After multiple screening, the core PPI network isfinally obtained.
2.5. GO Functional Enrichment Analysis and KEGG PathwayEnrichment Analysis. /e core target of PPI network se-lected above was imported into Metascape (https://metascape.org/gp/index.html, updated on March 20,2020) [26] database for KEGG (Kyoto Encyclopedia ofGenes and Genomes) pathway analysis and GO (GeneOntology) biological process enrichment analysis. Param-eter is set to min overlap >3, p value cutoff <0.01, and minenrichment >1.5. Taking p value as parameter and sortingfrom small to large as screening condition, KEGG pathwayand GO biological process of the top 20 eligible were selectedand uploaded to OmicShare (http://www.omicshare.com/tools) platform for data visualization.
2.6. Constructing PPI Network of Ingredient-Disease-KEGGPathway. /e top 20 KEGG pathways, Chaihu-Shugan-Sanactive ingredients, and disease common targets wereuploaded to Cytoscape 3.7.1 software to obtain the multi-dimensional network diagram of component-disease-KEGG pathway.
3. Results
3.1. Active Ingredient and Target of Chaihu-Shugan-San.OB ≥30%, DL ≥0.18 as the screening conditions, aftersearching TCMSP database, ChaiHu-ShuGan-San obtaineda total of 157 chemical components, 13 compounds fromBaishao, 17 compounds from Chaihu, 5 compounds fromChenpi, 7 compounds from Chuanxiong, 5 compoundsfrom Zhiqiao, 92 qualified compounds from Gancao, and 18compounds from Xiangfu (as shown in Table S1 in Sup-plementary Materials). 8074 targets were obtained by in-putting 158 chemical components into Swiss TargetPrediction online database.
3.2. Compound-Target Network Construction. /e com-pound-target network consists of 945 nodes and 8200 edges.29 of 157 compounds were not found in the database and notinvolved in the network construction (Figure 1). In thisnetwork, the average degree value is 15.647, and most of theproteins share common ligands with other proteins, whichreflects the mechanism of the joint action between multi-components and multitargets of Chaihu-Shugan-San, andconform to the characteristics of the traditional Chineseformula. Table 1 shows the detailed topological parametersof the top 10 compounds with high DC.
3.3. Screening of Breast Cancer Targets. Breast cancer ormalignant breast tumors were used as keywords to search inTTD, OMIM, DrugBank, and GeneCards databases. 37disease targets were obtained from TTD database, 1163disease targets were screened from OMIM database, 202
disease targets were screened from Drugbank database, and1286 disease targets with score >13.96 were obtained fromGeneCards database. /e duplicates were deleted aftermerging, and 2,384 breast cancer-related targets were finallyobtained.
3.4. Construction of PPI Network of Chaihu-Shugan-San andDisease Targets. To further explore the pharmacologicalmechanism of Chaihu-Shugan-San on breast cancer,Chaihu-Shugan-San and breast cancer protein were inputinto the BisoGenet plug-in of Cytoscape 7.2.1 software formerging. CytoNCA plug-in performs topological analysisand takes 2 times of the average degree value as the screeningcondition. In the first screening, a network composed of2,728 nodes and 109,005 edges was obtained by the medianDC >46. Finally, a PPI network with 451 nodes and 17,140edges was constructed by further screening with the medianDC >156. /e process is shown in Figure 2. Topologicalparameters of the top 10 targets with high DC are shown inTable 2, and other detailed results are shown in Table S2.
3.5. GO Biological Process and Enrichment Analysis of KEGGPathway. GO biological process consists of molecularfunction (MF), biological process (BP), and cellular com-ponent (CC) to interpret antitumor biological processes atkey targets. Kyoto Encyclopedia of Genes and Genomes(KEGG) enrichment analysis studies the key target of an-titumor signaling pathways. /e results of GO enrichmentanalysis showed that there were 2000 biological processes,274 cell components, and 564 molecular functions. KEGGenrichment has 154 signaling pathways. According to theranking of p values, the top 20 were selected to plot thebubble chart (Figure 3). /e left side of each chart is the topenriched name. /e color of bubbles from blue to redrepresents the p value from large to small. /e larger thebubbles, the larger the gene count of the pathway. /ehorizontal axis represents the ratio of the pathway genes tothe total input genes. /e top 20 signal pathways of KEGGenrichment are shown in Table S3.
3.6. 7e Multidimensional Network of “Component-TargetDisease-KEGG Signaling Pathway” Was Constructed.Combining the component-target network and the first 20KEGG signaling pathway targets, a multidimensional net-work of “component-disease target-KEGG signaling path-way” was obtained by Cytoscape 7.2.1 software (as shown inFigure 4). /e results showed that the effective componentsof Chaihu-Shugan-San could treat breast cancer by multi-target and multisignal pathways.
4. Discussion
Traditional Chinese medicine compound acts on diseasesthrough multimolecule, multitarget, and multipathway andplays a certain therapeutic effect. Network pharmacology is ascience based on the macroconnection under the back-ground of the big data era. It systematically analyzes the
Evidence-Based Complementary and Alternative Medicine 3
molecular mechanism of action from all levels, which isconsistent with the holistic view of TCM and the thought ofsyndrome differentiation and treatment. Chaihu-Shugan-San prescription, in which Chaihu is the monarch drug, isgood at soothing the “Liver-Qi.” Xiangfu and Chuanxiongare the minister drugs, which can relieve “Liver-Qi.” Chenpiand Zhiqiao, regulating “Qi” stagnation and Baishao,nourishing “Blood” and softening the “Liver,” are adjuvants.Gancao is used as a guide medicine to reconcile variousdrugs./e combination of various drugs can regulate “Liver-Qi” and smooth “Qi.” Chaihu-Shugan-San has a history ofmore than 480 years. /e basic compatibility of clinicalmedication is Chenpi 6 g, Chaihu 6 g, Chuanxiong 4.5 g,Xiangfu 4.5 g, Zhiqiao 4.5 g, Shaoyao 4.5 g, and Gancao 1.5 g,which is added or subtracted according to the actual situ-ation of patients. /e official preparation method is to add250ml of water to the herbs and boil them for 30min. Su[27] and his team identified 33 chemical constituents inChaihu-Shugan-San. Among them, gallic acid (source:Shaoyao), oxidized paeoniflorin (source: Shaoyao), paeo-niflorin (source: Shaoyao), paeoniflorin (source: Shaoyao),glycyrrhizin (source: Gancao), naringin (source: Chenpi,Zhiqiao), hesperidin (source: Chenpi, Zhiqiao), and ferulicacid (source: Chuanxiong) had higher contents, all above1000mg/g [28]. Although some studies have comprehen-sively elucidated the treatment of depression [6, 29], non-alcoholic fatty liver disease [30, 31] and functional dyspepsia[5] by Chaihu-Shugan-San, no studies have comprehen-sively elucidated the mechanism of Chaihu-Shugan-San inthe treatment of breast cancer. /erefore, with the aid ofnetwork pharmacology, this study analyzed the specificmolecular mechanism of Chaihu-Shugan-San in the treat-ment of breast cancer from a microscopic perspective.
/e results of network analysis showed that the activeingredients in Chaihu-Shugan-San mainly included beta-sitosterol, kaempferol, quercetin, naringenin, isorhamnetin,and nobiletin. Beta-sitosterol can promote the apoptosis of
breast cancer cells by activating the Fas signaling pathwayand caspase-8 activity [32] and is expected to be an orphannutrition drug against cancer [33]. Kaempferol has shown agood affinity for PAK4 in molecular docking and is con-sidered to be a potential inhibitor in triple-negative breastcancer [34], and kaempferol can prevent G2/M phase of thecell cycle by downregulating CDK1 in human breast cancerMDA-MB-453 cells [35], and blocking RhoA and Rac1signaling pathways to inhibit breast cancer cell migrationand invasion [36] is a powerful antioxidant inducer and caninhibit oncogene transformation and induce cancer cellapoptosis and DNA damage. Quercetin, naringenin, andisorhamnetin, such as flavonoids, can prevent breast cancercell migration through inflammatory and apoptotic cellsignaling [37, 38], and quercetin can induce autophagy byinhibiting the Akt-mTOR pathway [39].
/e PPI network showed that the active components inChaihu-Shugan-San may function through the core targetssuch as histone deacetylase 1 (HDAC1), huntingtin (HTT),RAC-alpha serine/threonine-protein kinase (AKT1), hepa-toma-derived growth factor (HDGF), roquin-1 (RC3H1),chromobox protein homolog 8 (CBX8), histone deacetylase
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AVPR1A
ITGAL
MAPK3
SRC
PKN1
SCN5A
ADAMTS5PLAU
HDAC6
CA9
PTP4A3P2RX3
BMP4
AKR1C2
DPYD
MAP3K5
HDAC9
ADRA1D
F2RLIPE
CASK
CYP19A1
CASP7
RPS6KA1
LTA4H
GPR35
CSNK1D
CA7 KDM4E MGMTSHBGSCARB1
TAAR1
EGFR
PTGES
FGFR3
TBXAS1
MPEG1
RPS6KA3
CCNT1
TDP2
PLK4
CYP1A1
PSEN1
MTNR1A
CNR2
CDC42BPA
TACR2
MAPKAPK5
ADCY5
PDE10A
GCK
PDGFRA
IRAK4
COMT
MMP7
FGFR1
CTSB
MARS
SRD5A1
DRD5
CCNA2
RBP4CLK2
PRKCD
AKR1C3
RELA
PLAA
OXTR
GRM5
TGM2
OPRD1
OGA
HSP90B1
GRM1
FFAR1
CYP11B2
KDM5C
OPRM1
SLC19A1
CCR2
CES2
PPIA
ZQ
MOL005100
MMP3
PFKFB3
KAT2B
KDM1A
CDK4
PDE3A
TNF
CSK
SHH
SLC18A2
AKR1C4
ADAM9 CAPN2
CDK1
CTSD
SSTR5
ODC1
DBF4
ABCB1
SPHK2
PDGFRB
TNNI3K
BACE1
GRIN2A
GSTA1
NPY5R
ESR2
EPAS1
BMP1
DHCR7
PDE6C
FOLR2
PGF
LRRK2
FGF1
KDM4C
PRKCB
SIRT1
ERCC5
ABHD6
MOL004058
MOL004068 MOL004053
XFMOL004071
MOL004074
MOL000006
MOL000449
GLI1 ADORA2ATGFBR1
EPHA3ESRRB EPHB1
MOL003044
MAP3K8FLT3
SPHK1
DYRK1A
PTPN11 NAE1
CSNK1G1NR3C1
LGALS7
MAP2K3
PDE1A
IGFBP6
MGAT2
SERPINA6
LYPLA1
ALOX12
G6PDCALM1
SLC28A2
NEK6
VEGFA
ERBB2
ATR
CAPN1
TLR9
CA5B
HSD11B2
ESR1
GLO1
CCR1
ADORA3
HIF1A
DNMT1
DYRK2
MT-CO2
STK17B
CDK7
HSD11B1
P2RX7
PON1
AKR1A1
TAOK1
NOS1
MOL002135
ADAMTS4
MAP2K1
GABRA2
CD81
METAP2
PRKD2
CASP3
UTS2R
NEK2
ST6GAL1
CHRM3
AKR1B1
JAK2
ALOX15B
DSTYK
HSPA5
PROKR1
PRKD1
PABPC1
CLK3
KDM5A
AGTR1
DNM1
PTGER4
LDHA
RPS6KA5
CTSL
S1PR3
MBD2
CYP1A2
CSNK1A1
ILK
AKR1B10
KCNH2
CHRM2
LYN
PLA2G1B
TTR
CDK5R1
FOLH1
FLT1
FABP3
TAOK3
STAT3
MMP9
PNP
VDR PTGS1
F3 IGF1R
EIF4H
CHRM1
ABL1CCR4
EIF2AK3
COQ8B
ADCY10
DMPK
PLEC
ROCK2
EPHB4
HDAC10
SLC6A9
REN
KCNA5
CA5A
NCOR1
PTPN2
SNCA
DUT
ACPP
IGFBP5
SLC46A1
POLB
PLG
PTAFR
EDNRB
Figure 1: Compound-target network: green diamond nodes represent compounds, red diamond nodes represent common compounds,yellow v nodes represent herb names, and purple circular nodes represent corresponding potential targets of compounds.
Table 1: Topological parameter of top 10 compounds.
ID Molecule name DC BC CC ASPLMOL000422 Kaempferol 317 0.011 0.377 2.651MOL000354 Isorhamnetin 303 0.008 0.373 2.681MOL000359 Sitosterol 233 0.005 0.360 2.774MOL004328 Naringenin 199 0.058 0.397 2.520MOL000358 Beta-sitosterol 139 0.004 0.357 2.802MOL000098 Quercetin 116 0.009 0.377 2.654MOL004609 Areapillin 101 0.008 0.372 2.690MOL003044 Chrysoeriol 101 0.008 0.371 2.690MOL000006 Luteolin 101 0.008 0.371 2.690MOL004071 Hyndarin 101 0.063 0.374 2.677
4 Evidence-Based Complementary and Alternative Medicine
2 (HDAC2), small ubiquitin-related modifier 1 (SUMO1),40 S ribosomal protein SA (RPSA), and 60 S acidic ribosomalprotein P0(RPLP0). HDAC1 plays an important role intranscriptional regulation and cell cycle progression [40].HDAC1 can promote the proliferation and migration ofbreast cancer cells by activating the Snail/IL-8 signalingpathway [41]. Downregulation of HTT transcription andprotein levels is a key factor in poor prognosis andmetastasisdevelopment of breast cancer [42]. AKT1 is involved in theregulation of many tumor processes, including tumorproliferation, cell survival, metabolism, growth, and an-giogenesis. /e mutation frequency of AKT1 in Chinesebreast cancer patients is 3.2%, and it is considered to be asensitive target for the treatment of breast cancer. A studyinvolving 313 Chinese breast cancer patients found that themutation frequency of AKT1 in Chinese breast cancer pa-tients was 3.2%, and it is considered a sensitive target for thetreatment of breast cancer [43]. HDAC2 is a poor prognosticfactor in patients receiving anthracycline therapy and ispositively correlated with breast cancer metastasis, pro-gression, increased Ki-67, multidrug resistance protein, andnegatively correlated with overall survival of patients [44]./e occurrence and development of breast cancer are closelyrelated to the core proteins, which fully prove that the
treatment of breast cancer by Chaihu-Shugan-San is theresult of multimolecular, multitarget, and multipathwayinteraction.
/e biological process of Gene Ontology (GO) involvesthe mRNA catabolic process, RNA catabolic process, telo-mere organization, nucleobase-containing compound cat-abolic process, heterocycle catabolic process, and so on. Incellular component, cytosolic part, focal adhesion, cell-substrate adherens junction, and cell-substrate junction arehighly correlated with breast cancer. In the molecularfunction category, most proteins were addressed to ubiq-uitin-like protein ligase binding, protein domain specificbinding, and Nop56p-associated pre-rRNA complex. Inaddition, the results of KEGG pathway analysis showed thatthe pathways mainly involved in apoptosis, cell cycle,transcriptional dysregulation, endocrine resistance, and viralinfection. Estrogen receptor (ER) signal transductionpathway plays a central role in the development of breastcancer. ER can not only regulate the expression of certaingenes through its mediated signal transduction pathway butalso has extensive connections with many other signaltransduction pathways, forming a signal transduction reg-ulatory network [45]. ER binds to receptor proteins in thenucleus, and the receptor is activated. Activated ER-α andER-β form homodimers or heterodimers. Some coregulatorsform complexes with dimers. /e complexes bind to ERresponse elements to initiate transcription, thereby regu-lating the function of target genes, leading to abnormal cellproliferation and differentiation, and ultimately leading totumorigenesis [46]. /e increased mutation rate of ER-α inprecancerous lesions of breast cancer affects the junctionbetween ER-α zinc finger region and ligand binding domain,resulting in high sensitivity of the body to estrogen. Underthe action of low levels of hormones, ER-α is highly bound toTNF-2 co-activator, which leads to the occurrence of tu-mors. For ER receptor-positive breast cancer patients,quantitative expression of ER receptor is an independentimaging factor to evaluate their prognosis, recurrence, andmetastasis [47]. Abnormal activation of MAPK signal
451 nodes and 17140 edges2728 nodes and 109005 edges9787 nodes and 215324 edges
DC
> 46
DC
> 15
6
Figure2: Network topology analysis of PPI.
Table 2: Topological parameter of top 10 core targets.
Target DC BC CC ASPLHDAC1 1976 0.075 0.534 1.874HTT 1695 0.094 0.526 1.900AKT1 1136 0.031 0.495 2.019HDGF 1095 0.021 0.499 2.002RC3H1 930 0.026 0.501 1.996CBX8 836 0.013 0.488 2.050HDAC2 813 0.015 0.493 2.029SUMO1 810 0.021 0.500 1.999RPSA 761 0.013 0.489 2.047RPLP0 736 0.011 0.481 2.081
Evidence-Based Complementary and Alternative Medicine 5
transduction pathway can lead to cell loss of apoptosis anddifferentiation ability, promote malignant transformation,abnormal proliferation, produce tumors, and further pro-mote the proliferation of tumor cells./erefore, inhibitors ofsome key kinases in the MAPK signaling pathway havebecome a hotspot in the treatment of breast cancer in recentyears. Studies have found that Kruppel-like factor 4 [48] andpre-mRNA processing factor 4 [49] affect the growth,
migration, and apoptosis of breast cancer cells throughMAPK and are expected to become new targets for thetreatment of breast cancer. Studies found that the activationor loss of FOXO function can inhibit the growth and me-tastasis of breast tumors [50], and the dysregulation ofFOXO transcription factors has also become a key moleculein the endocrine resistance mechanism [51]. More andmore attention has been paid to the relationship between
GO:0072594:establishment of proteinlocalization to organelle
GO:0006402:mRNA catabolic process
GO:0006401:RNA catabolic process
GO:0032200:telomere organization
GO:0034655:nucleobase−containing compound catabolic process
GO:0046700:heterocycle catabolic process
GO:0044270:cellular nitrogen compound catabolic process
GO:0019439:aromatic compound catabolic process
GO:1901361:organic cyclic compound catabolic process
GO:0010564:regulation of cell cycle process
GO:0097190:apoptotic signaling pathway
GO:0065004:protein−DNA complex assembly
GO:0006281:DNA repair
GO:0000184:nuclear−transcribed mRNA catabolicprocess, nonsense−mediated decay
GO:0019083:viral transcription
GO:0044257:cellular protein catabolic process
GO:0019941:modification−dependent protein catabolic process
GO:1903320:regulation of protein modificationby small protein conjugation or removal
GO:0019221:cytokine−mediated signaling pathway
GO:0071824:protein−DNA complex subunit organization
0.1 0.2 0.3 0.4
Rich factor
GO
term
Top 20 of GO enrichment
Gene number
3.457230e − 631.986787e − 433.973575e − 435.960362e − 437.947150e − 43
p value
50
60
70
80
90
(a)
GO:0044445:cytosolic part
GO:0005925:focal adhesion
GO:0005924:cell−substrate adherens junction
GO:0030055:cell−substrate junction
GO:0005912:adherens junction
GO:0070161:anchoring junction
GO:0022626:cytosolic ribosome
GO:0032993:protein−DNA complex
GO:0098687:chromosomal region
GO:0000784:nuclear chromosome, telomeric region
GO:0000781:chromosome, telomeric region
GO:0000788:nuclear nucleosome
GO:0005840:ribosome
GO:0016604:nuclear body
GO:0044391:ribosomal subunit
GO:1990234:transferase complex
GO:0022627:cytosolic small ribosomal subunit
GO:0000786:nucleosome
GO:0044815:DNA packaging complex
GO:0015935:small ribosomal subunit
0.2 0.4 0.6
Rich factor
Top 20 of GO enrichment
Gene number
4.98804e − 501.28158e − 212.56316e − 213.84474e − 215.12632e − 21
p value
30
40
50
60
70G
O te
rm
(b)
GO:0044389:ubiquitin−like protein ligase binding
GO:0031625:ubiquitin protein ligase binding
GO:0019904:protein domain specific binding
CORUM:3055:Nop56p−associated pre−rRNA complex
GO:0019900:kinase binding
GO:0019901:protein kinase binding
GO:0008134:transcription factor binding
CORUM:306:ribosome, cytoplasmic
GO:0045296:cadherin binding
GO:0050839:cell adhesion molecule binding
GO:0003735:structural constituent of ribosome
GO:0046982:protein heterodimerization activity
CORUM:338:40S ribosomal subunit, cytoplasmic
CORUM:305:40S ribosomal subunit, cytoplasmic
GO:0003682:chromatin binding
GO:0042826:histone deacetylase binding
GO:0051427:hormone receptor binding
CORUM:5266:TNF−alpha/NF−kappa B signaling complex 6
GO:0005198:structural molecule activity
GO:0001085:RNA polymerase II transcription factor binding
0.00 0.25 0.50 0.75
Rich factor
GO
term
Top 20 of GO enrichment
Gene number
2.554400e − 781.066667e − 192.133335e − 193.200003e − 194.266670e − 19
p value
25
50
75
100
(c)
hsa05203:viral carcinogenesis
hsa05169:Epstein−Barr virus infection
hsa05161:hepatitis B
hsa05200:pathways in cancer
hsa05034:alcoholism
hsa05205:proteoglycans in cancer
hsa04110:cell cycle
hsa03010:ribosome
hsa05220:chronic myeloid leukemia
hsa05168:herpes simplex infection
hsa04120:ubiquitin mediated proteolysis
hsa05212:pancreatic cancer
hsa05166:HTLV−I infection
hsa05215:prostate cancer
hsa04210:apoptosis
hsa04919:thyroid hormone signaling pathway
hsa04010:MAPK signaling pathway
hsa04068:FoxO signaling pathway
hsa05202:transcriptional misregulation in cancer
hsa01522:Endocrine resistance
0.1 0.2 0.3 0.4
Rich factor
Top 20 of KEGG Enrichment
Gene number
4.782950e − 671.462308e − 222.924615e − 224.386922e − 225.849230e − 22
p value
30
40
50
60
Path
way
(d)
Figure 3: GO function enrichment analysis and enrichment analysis of KEGG signaling pathway (top 20). (a) BP. (b) CC. (c) MF. (d)KEGG.
6 Evidence-Based Complementary and Alternative Medicine
viral infection and breast cancer. In particular, humanpapilloma virus (HPV) has a strong cause-and-effect re-lationship with breast cancer. Many studies have found thatdifferent HPV genotypes are associated with the prevalenceof breast cancer and the nuclear prognosis./e relationshipbetween viral infection and breast cancer has been paidmore and more attention [52–54]. /e relationship be-tween Epstein–Barr Virus (EBV) and breast cancer has alsobeen extensively studied, but the current evidence is lessandmore controversial [55]. It is proved again that Chaihu-Shugan-San treatment of breast cancer is through acombination of multiple biological pathways and multiplesignaling pathways, but this multifeature is not only foundin a single disease. Chaihu-Shugan-San is mainly involvedin the regulation of neurotransmitters, regulation of in-flammatory mediators of TRP channels, calcium signalingpathways, cyclic adenosine monophosphate signalingpathways, and neuroactive ligand-receptor interactions toplay an antidepressant role [56]. Chaihu-Shugan-San canimprove cognitive impairment in Alzheimer’s diseasethrough multitarget action, and its effect is verified bybiological experiments [57]. /ese all embody the principleof “treating different diseases with the same treatment” inTCM.
Data Availability
All data generated or analyzed during this study are includedin this paper.
Disclosure
Kunmin Xiao and Kexin Li are the first authors.
Conflicts of Interest
All authors state that they have no conflicts of interest re-garding the publication of this paper.
Authors’ Contributions
Shijie Zhu conceived and designed the experiments. KunminXiao and Kexin Li performed the experiments and wrote themanuscript. Sidan Long and Chenfan Kong contributed toanalysis tools.
Acknowledgments
/is work was supported by grants from the China NationalNatural Science Foundation (Grant no. 81973640).
Supplementary Materials
Table S1: active ingredients of Chaihu-Shugan-San. Table S2:topological parameters of Chaihu-Shugan-San targets. TableS3: the top 20 signal pathways of KEGG enrichment.(Supplementary Materials)
References
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LYNRAC1
YWHAHH4C9
RB1
YWHABTRAF2
YWHAEYWHAG
YWHAZUSP7H2BC8H2BC10H4C4H4C12H4C11H4C8H4C2
IKBKGH4C1
H4C3 H4C6HDAC3KAT2BSNW1
H4-16CSNK2B
H4C5H4C13
H4C14YWHAQ
HSPB1HSPA1B
CSNK2A2CHUK
H4C15
HSPA8
PSMC5
HSPA1AICAM1
MYC
NEDD4
POLR2A
PML
PTEN
CUL2
SHC1
CANX
MAP3K3
PSMC3
PSMD2
PSMD4
TRAF6
VIM
XPO1
FOS
SMAD3
CRKCTNNB1
FN1HSP90AB1
SMAD2
SMAD4
MAP3K1
NFATC2
PCNAAPC
CBL
VCAM1
KAT5
PRMT1
PRKAB1
BRCA1
Pathways in cancer
PPP2CB
CDC37RPS6PPP2R1A
FoxOsignalingpathway
ChronicmyeloidleukemiaSignalingpathwaysregulating
pluripotencyof stem
cells Proteoglycansin cancer
Adherensjunction
Prostatecancer
Colorectalcancer
HTLV-Iinfection
Bacterialinvasion of
epithelialcells
PPP1CBPPP1CC
H3-4H3C1
H3C11H3C3
ITGA4PPP2CARPS6KB2POU5F1BNANOGCOMMD3-BMI1PPP1CA
SOX2POU5F1SMAD1BMI1
H3C4H3C6
H3C8
H3C12
H3C2
H3C7
VHL
FBXO25RBX1 TNFRSF1A
ERBB3
CAV1
ACTG1
ACTBH3C10
FYN
HDAC5
STAT3
CASP8AKT1
NFKB1CREBBP
SRC
ABL1
PLK1
PRKACA
PKM
JUN
CDK4
IKBKB
AR
ESR1
EZR
ERBB2
GSK3B
RELA
MDM2RAF1
NTRK1
CDK2
TGFBR1
ILK
EP300
EGFR
CASP3
HIF1A ATM
PLCG1
HDAC4
SIRT1CDKN1A
CDKN2A
ATF2
DDB1
CDC42
MYH9
AGE-RAGEsignaling
pathway in diabetic
complications
PI3K-Aktsignalingpathway
Hepatitis B
Systemiclupus
erythematosus
Viralcarcinogenesis
Epstein-Barrvirus
infection
Alcoholism Pancreaticcancer
Focaladhesion
Regulationof actin
cytoskeleton
GRB2
H2BC5
HLA-B
NFKBIA
PXN
REL
TRAF1
HNRNPK
DDX3X
IQGAP1MAPK3
HDAC6
MAPK1MAPK14
PIK3R1HDAC2
PRKCA
HSP90AA1
STAT1
PTPN11
CDK1
IKBKE
DDX5 MAPK8HDAC1 PRKCD
FLNA
VCL
SSX2IP
CLTC
SLC19A1
DPYD
HSD17B1
CCND1
LGALS3
PPARG
PRKCQ
PIK3CA
CCND3
RARA
CHRNA3
BTK
CYP19A1
LDHA
JAK1
SPHK2
FGF2
SLC29A1
SMARCA2
EDNRB
NOS2
PRKD1
AURKASLC6A2
FGFR1TKT
MOL000211
MOL004841
MOL005000
MOL004966
MOL000239
MOL004882
MOL004833MOL004974
MOL004058MOL000098 MOL004978MOL004848
MOL004898MOL004904
MOL004071
MOL004838
MOL000359
MOL004864
MOL004935
MOL004959MOL004913
MOL004828
MOL001484
MOL000500
MOL004849
MOL001923
MOL004908
MOL000497
MOL005012
MOL005003
MOL004053
MOL000422
RPS6KB1
STAT6
MMP9
PSEN2
TNF
MAP2K1
MYLK
IGFBP4
CYP17A1F2R HSD17B3GRM1
GLI2FGFR2
CYP1A1ROCK1PDGFRACSK
PLGNR3C2MET MME
EPHA4RARG
ABCG2SPHK1
PKN1
KDM1A
SYK
CYP3A4
SLC2A1
XIAP
GNRHR
ALK
APEX1MIF
PIK3CGPRKCB
NQO2
PON1CCNE1EPHB4CYP1B1CA9ADAM17MGMT
PTGS1
ATR
FLT4
WNT3APIK3CB
MMP14
SRD5A1
AKT2ESRRG
ERCC5
IL2
FOLH1SSTR2
CXCL8BMP4
KCNH2
JAK2
HPGD
AXL
CXCR2
DRD2MAP2K4SHH
BCL2L1
PDGFRB
KDRNCOR1
S1PR3PTPRFMAP3K8
ABCB1
EZH2
PLA2G2ABRS3
CXCR4CSNK1D
TERT
ANPEPIGFBP3
MMP7GLI1MCL1ADA CDC25BTEK FLT3GPER1
PLA2G10MDM4
GSTP1
IGFBP1
AKR1C3
CHEK2
PTK2
IGF1R
CDC25AKISS1RDAPK1CTSL
ROS1PTPN1HMGCRPLK3
BMP1NOS3
TGFB1WEE1
SLC6A4
PIK3CD
CCKBRG6PD
SLC6A3IDH1
CA2
HSPA5
KIT
RPS6KA3
VCP
CBFBF2
SREBF2EPHA2
BRAFHPSE
CTSDCALCAMPOTYMSFABP4SERPINE1CDK7CDC25C
LIMK1FGFR3
NCOR2NR1H2
COMT
TLR9ABCC1
CASP7ESR2
TYMPVEGFATOP1EPHB2AHRALDH2DUSP3
HDAC9TTR
JAK3DNMT1
IGFBP5
CTSBMMP2
VDR
BAD AURKB
MAP2K2DHFR
TLR4KDM4C
IGFBP2CYP2D6
PGR
PRSS1
MAPK9CYP11B1
HSD11B2
MTOR
PLAU
CCNE2
TOP2A
F3
EDNRA
CDK6
SHBG
CYP1A2
CCNA2
PTK2B
CFTR
CHEK1
PARP1
ALOX5
PTGS2
NR3C1
ODC1
FGF1
BCL2
EPHA3
TYR
RET
SRD5A2
FLT1
MMP1MOL004911
MOL004879
MOL001494
MOL004991
MOL004924
HRAS
FABP3
MMP3
CXCR3
GAPDH
PTK6
TGM2
MAPK10
RARB
ESRRAROCK2
FASN
MOL000392
MOL004996
MOL004814
MOL000433
MOL004885
MOL002311
MOL003896
MOL004985
MOL004905
MOL004910
MOL002565
MOL001792
MOL004856
MOL004857
MOL003656
MOL001919
MOL002341
MOL013187 INSR
STS
PDPK1
EPAS1
MMP13
CSF1R
Figure 4: Component-disease target-KEGG signaling pathway. Green represents active ingredients of drugs; purple represents targets;yellow represents common targets; and red represents signaling pathways.
Evidence-Based Complementary and Alternative Medicine 7
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