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dc.contributor.authorYang, Williamen_US
dc.contributor.authorYoshigoe, Kenjien_US
dc.contributor.authorQin, Xiangen_US
dc.contributor.authorLiu, Jun Sen_US
dc.contributor.authorYang, Jack Yen_US
dc.contributor.authorNiemierko, Andrzejen_US
dc.contributor.authorDeng, Youpingen_US
dc.contributor.authorLiu, Yunlongen_US
dc.contributor.authorDunker, A Keithen_US
dc.contributor.authorChen, Zhongxueen_US
dc.contributor.authorWang, Liangjiangen_US
dc.contributor.authorXu, Dongen_US
dc.contributor.authorArabnia, Hamid Ren_US
dc.contributor.authorTong, Weidaen_US
dc.contributor.authorYang, Mary Quen_US
dc.date.accessioned2015-03-02T17:38:23Z
dc.date.issued2014en_US
dc.identifier.citationYang, W., K. Yoshigoe, X. Qin, J. S. Liu, J. Y. Yang, A. Niemierko, Y. Deng, et al. 2014. “Identification of genes and pathways involved in kidney renal clear cell carcinoma.” BMC Bioinformatics 15 (Suppl 17): S2. doi:10.1186/1471-2105-15-S17-S2. http://dx.doi.org/10.1186/1471-2105-15-S17-S2.en
dc.identifier.issn1471-2105en
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:14065393
dc.description.abstractBackground: Kidney Renal Clear Cell Carcinoma (KIRC) is one of fatal genitourinary diseases and accounts for most malignant kidney tumours. KIRC has been shown resistance to radiotherapy and chemotherapy. Like many types of cancers, there is no curative treatment for metastatic KIRC. Using advanced sequencing technologies, The Cancer Genome Atlas (TCGA) project of NIH/NCI-NHGRI has produced large-scale sequencing data, which provide unprecedented opportunities to reveal new molecular mechanisms of cancer. We combined differentially expressed genes, pathways and network analyses to gain new insights into the underlying molecular mechanisms of the disease development. Results: Followed by the experimental design for obtaining significant genes and pathways, comprehensive analysis of 537 KIRC patients' sequencing data provided by TCGA was performed. Differentially expressed genes were obtained from the RNA-Seq data. Pathway and network analyses were performed. We identified 186 differentially expressed genes with significant p-value and large fold changes (P < 0.01, |log(FC)| > 5). The study not only confirmed a number of identified differentially expressed genes in literature reports, but also provided new findings. We performed hierarchical clustering analysis utilizing the whole genome-wide gene expressions and differentially expressed genes that were identified in this study. We revealed distinct groups of differentially expressed genes that can aid to the identification of subtypes of the cancer. The hierarchical clustering analysis based on gene expression profile and differentially expressed genes suggested four subtypes of the cancer. We found enriched distinct Gene Ontology (GO) terms associated with these groups of genes. Based on these findings, we built a support vector machine based supervised-learning classifier to predict unknown samples, and the classifier achieved high accuracy and robust classification results. In addition, we identified a number of pathways (P < 0.04) that were significantly influenced by the disease. We found that some of the identified pathways have been implicated in cancers from literatures, while others have not been reported in the cancer before. The network analysis leads to the identification of significantly disrupted pathways and associated genes involved in the disease development. Furthermore, this study can provide a viable alternative in identifying effective drug targets. Conclusions: Our study identified a set of differentially expressed genes and pathways in kidney renal clear cell carcinoma, and represents a comprehensive computational approach to analysis large-scale next-generation sequencing data. The pathway and network analyses suggested that information from distinctly expressed genes can be utilized in the identification of aberrant upstream regulators. Identification of distinctly expressed genes and altered pathways are important in effective biomarker identification for early cancer diagnosis and treatment planning. Combining differentially expressed genes with pathway and network analyses using intelligent computational approaches provide an unprecedented opportunity to identify upstream disease causal genes and effective drug targets.en
dc.language.isoen_USen
dc.publisherBioMed Centralen
dc.relation.isversionofdoi:10.1186/1471-2105-15-S17-S2en
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC4304191/pdf/en
dash.licenseLAAen_US
dc.subjectKidney Renal Clear Cell Carcinomaen
dc.subjectTCGAen
dc.subjectRNA-Seqen
dc.subjectDifferentially Expressed Genesen
dc.subjectPathwaysen
dc.subjectGene Network Analysisen
dc.subjectMachine Learning Classifieren
dc.titleIdentification of genes and pathways involved in kidney renal clear cell carcinomaen
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden
dc.relation.journalBMC Bioinformaticsen
dash.depositing.authorLiu, Jun Sen_US
dc.date.available2015-03-02T17:38:23Z
dc.identifier.doi10.1186/1471-2105-15-S17-S2*
dash.authorsorderedfalse
dash.contributor.affiliatedNiemierko, Andrzej
dash.contributor.affiliatedLiu, Jun


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