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dc.contributor.authorChen, L.
dc.contributor.authorYuan, L.
dc.contributor.authorQian, K.
dc.date.accessioned2019-06-05T18:28:16Z
dc.date.available2019-06-05T18:28:16Z
dc.date.issued2018
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85045521944&doi=10.3389%2ffphys.2018.00399&partnerID=40&md5=66518d3e6afae1e4205936f84833b86a
dc.identifier.urihttp://hdl.handle.net/10713/9424
dc.description.abstractClear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cancer whose prognostic is affected by the tumor progression associated with complex gene interactions. However, there is currently no available molecular markers associated with ccRCC progression and used or clinical application. In our study, microarray data of 101 ccRCC samples and 95 normal kidney samples were analyzed and 2,425 differentially expressed genes (DEGs) were screened. Weighted gene co-expression network analysis (WGCNA) was then conducted and 11 co-expressed gene modules were identified. Module preservation analysis revealed that two modules (red and black) were found to be most stable. In addition, Pearson's correlation analysis identified the module most relevant to pathological stage(patho-module) (r = 0.44, p = 3e-07). Functional enrichment analysis showed that biological processes of the patho-module focused on cell cycle and cell division related biological process and pathway. In addition, 29 network hub genes highly related to ccRCC progression were identified from the stage module. These 29 hub genes were subsequently validated using 2 other independent datasets including GSE53757 (n = 72) and TCGA (n = 530), and the results indicated that all hub genes were significantly positive correlated with the 4 stages of ccRCC progression. Kaplan-Meier survival curve showed that patients with higher expression of each hub gene had significantly lower overall survival rate and disease-free survival rate, indicating that all hub genes could act as prognosis and recurrence/progression biomarkers of ccRCC. In summary, we identified 29 molecular markers correlated with different pathological stages of ccRCC. They may have important clinical implications for improving risk stratification, therapeutic decision and prognosis prediction in ccRCC patients. Copyright 2018 Chen, Yuan, Qian, Qian, Zhu, Wu, Dan, Xiao and Wang.en_US
dc.description.sponsorshipThis study was supported in part by grants from the Hubei Province Health and Family Planning Scientific Research Project (grant number WJ2017H0002) and National Natural Science Foundation of China (grant number 81772730).en_US
dc.description.urihttps://dx.doi.org/10.3389/fphys.2018.00399en_US
dc.language.isoen-USen_US
dc.publisherFrontiers Media S.A.en_US
dc.relation.ispartofFrontiers in Physiology
dc.subjectClear cell renal cell carcinoma (ccRCC)en_US
dc.subjectDifferentially expressed genes (DEGs)en_US
dc.subjectPathological stageen_US
dc.subjectSurvival prognosisen_US
dc.subjectWeighted gene co-expression network analysis (WGCNA)en_US
dc.titleIdentification of biomarkers associated with pathological stage and prognosis of clear cell renal cell carcinoma by co-expression network analysisen_US
dc.typeArticleen_US
dc.identifier.doi10.3389/fphys.2018.00399


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