EditorialApplications of Bioinformatics and Systems Biology inPrecision Medicine and Immunooncology
Yudong Cai ,1 Tao Huang ,2 and Jialiang Yang 3
1School of Life Sciences, Shanghai University, Shanghai 200444, China2Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, China3Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York City, NY 10029, USA
Correspondence should be addressed to Yudong Cai; cai [email protected]
Received 25 March 2018; Accepted 25 March 2018; Published 30 September 2018
Next-Generation Sequencing (NGS) technology, oen seenas the foundation of precisionmedicine, has been successfullyapplied in oncology diagnostics and immunotherapy. Withadvances in gene diagnostics and immunotherapy, there maybe a chance to control the development of cancers and alle-viate the suffering of patients undergoing chemotherapy. Topromote the translation of precision medicine from bench tobeside and from application of genetic testing to personalizedmedicine, new analysis methods for NGS and genetic dataneed to be developed. For example, the NGS panel is quitedifferent from whole genome sequencing (WGS), focusingon fewer genes or regions but requiring greater precision andefficiency. For complex diseases, such as cancers, the drivergenes are usually a cluster of genes in a regulatory network.Graph theories, such as shortest path analysis and randomwalk algorithms, will help dissect genomewide interactionsinto key modules or paths whose dysfunction is associatedwith disease progression.
In this special issue, we have received 32 papers, out ofwhich 19 has been accepted for publication. �ese paperscould be generally divided into 3 categories including (1)computational models in identifying key biomarkers, path-ways, and networkmodules associatedwith cancers and otherdiseases, (2) validations of the mechanisms of key biomarkersand their applications in tumor diagnosis and treatment, and(3) other studies in predicting tumor evolution, drug-diseaseassociation, disease sequence alignment, and so on.
H. Choi and K. J. Na first identified survival-related genenetwork modules. By selecting representative genes fromsurvival-related modules, they developed a deep learning-based risk stratification model for lung cancer. �eir modelshowed high predictability for prognosis in independentdatasets and its predictive value was independent of clinicaland pathological features of lung cancer.
J. Zheng et al. proposed a method for constructing thepathway network of gene phenotype. Briefly, it firstly buildsa biological pathway network, and then the GeneRank algo-rithm was used to select disease-associated pathways. �eresults by gene expression data of breast cancer show that themethod proposes an effective way to identify reliable disease-associated pathways.
X. Li et al. presented a new computational method basedon the SimRank and density-based clustering recommendermodel for miRNA-disease association’s prediction (SRM-DAP). �e AUC of 0.8838 based on leave-one-out cross vali-dation and case studies suggested the excellent performanceof the SRMDAP in predicting miRNA-disease associations.SRMDAP could also predict diseases without any relatedmiRNAs and miRNAs without any related diseases.
S. Zhu et al. provided a sample-specific method that con-structs an index with individual-specific dynamical networkbiomarkers (DNB), which are defined as early warning index(EWI) for detecting predisease state of individual sample.
HindawiBioMed Research InternationalVolume 2018, Article ID 1427978, 2 pageshttps://doi.org/10.1155/2018/1427978
Based on microarray data of influenza A disease, 144 genesare selected as DNB and the 7th time period is defined as pre-disease state.
B. Xie et al. first identified differentially expressed genesbetween patients with poor chemotherapy reaction and pa-tients with good chemotherapy reaction, followed by GOand KEGG pathway analyses, PPI network analysis, survivalanalysis of hub genes, and miRNA target prediction. �isstudy provides a better understanding about gene expressionin drug resistance samples, which is potentially useful for thetreatment of osteosarcoma.
3. Validations of Key Biomarkers andTheir Applications inTumor Diagnosis and Treatment
H.Meng et al. investigated the prevalence and prognostic andclinicopathologic features of human papillomavirus-relatedoropharyngeal cancer in northeast China and elucidated theinvolvement of p16 in the tumorigenesis and progressionof oropharyngeal squamous cell carcinoma (OPSCC) from1470 OPSCC patients collected from 2000 to 2016. �ey alsodemonstrated that p16 expression is significantly associatedwith early stage primary OPSCCs and the patients with p16expression tend to show better survival following surgery andradiotherapy.
X. Wu et al. investigated the clinicopathologic and prog-nostic significance as well as the potential role of HMGB1 inthe development and progression of lung cancer.�eir resultssuggest that HMGB1 expression is significantly associatedwith lung cancer progression and might be a potentialprognosis and therapeutic marker for lung cancer.
H.-B. Xing et al. investigated whether the knockdown ofIL-6 enhances the gemcitabine sensitivity of PANC-1 cells.In their experiments, the knockdown of IL-6 induced apop-tosis and reduced cell proliferation and tumorigenicity andremarkably promoted the antitumor effect of gemcitabine.�e results suggest that combining shRNA targeting IL-6 andgemcitabine is a potential clinical approach for pancreaticcancer therapy.
R. Liu et al. detected RAS mutation, immunoscore, andPD-L1 expression in 60 Chinese metastatic colorectal cancer(mCRC) patients and suggested PD-L1 expression and RASstatus to be prognostic indicators for mCRC patients withpalliative operation.
X. Yu et al. investigated themechanism ofAEG-1 for regu-lating metastasis in hypoxia-induced ovarian carcinoma. By astatistical method comparing the protein amounts of AEG-1,HIF-1𝛼, and VEGF in ovarian cancer tissue specimens, theyhypothesized that AEG-1 associates with hypoxia in ovariancancer by regulating the HIF-1-alpha/NF-kappa-B/ VEGFpathway.
Y. Chen et al. examined PD-L1 and PD-1 expression inthymic epithelial tumor (TET) tissues from patients. �eyfound that PD-L1 expression levels were correlated with dis-ease progression. �ey also showed that PD-L1 mRNA levelswere also correlated with its immunohistochemistry staininglevels and thus can be used as alternative method to detectPD-L1 levels in TETs.
X. Wu et al. also revealed that glycyrrhizin reduces theactivity of JAK/STAT signaling pathway, which regulates theexpression of HMGB1. �is is a potential mechanism bywhich glycyrrhizin can inhibit the progression of lung cancer.
Z. Jiang et al. investigated the prognostic value of liverfunction in colorectal liver metastases patients. �ey showedthat biochemical analyses of liver function tests at the initialdiagnosis of colorectal liver metastases enable the stratifica-tion of patients into low- and high-risk groups, which mayhelp clinicians to determine promising treatment strategies.
4. Other Studies in Predicting TumorEvolution, Drug-Disease Association, andDisease Sequence Alignment
Y. Liang et al. proposed an improved binary differentialevolution algorithm, BDEP, to infer tumor phylogenetic treebased on fluorescent in situ hybridization (FISH) platform.�e constructed phylogenetic trees have great performancein characterizing tumor development process, which outper-forms other similar algorithms.
H. Cui et al. developed a method called PEDD to predictthe effect of drugs on diseases by using gene expressionprofiles fromboth disease-related tissues and cell lines treatedwith drugs. �e authors also implemented the method withan interactive web tool and applied the method to realmicroarray and RNA-seq datasets.
Finally, M. Li et al. proposed a novel protein sequencealignment method, which outperforms a few current align-ment methods in accuracy. Y. Luo et al. developed a com-putational method to investigate key GO terms and KEGGpathways associated with copy number variations (CNVs).Y. Fang et al. explored the genetic polymorphism of Aedesalbopictus and its association with diseases like dengue. AndL. Wang et al. studied the roles of swine as a mixtures forinfluenza viruses.
In summary, we expect that this special issue updatesnovel NGS-based computational methods in predicting keygenes, pathways, and network modules associated with dis-eases like cancers and experimental validations of theirmech-anisms, as well as their applications in disease diagnosis andtreatment. �e studies will serve as a bridge to connect com-putational models in mining clinical NGS data and transla-tion of the findings into personalized therapies for diseases.
Acknowledgments
We are grateful to the authors for contributing their valuablework to this special issue and the reviewers for this con-structive comments. We also thank the editorial board forapproving this topic and hope this issue will advance the re-search in disease-associated biomarker identification and itsapplications in translational research.
