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pmid:29720944
Identification of Biomarkers Associated With Pathological Stage and Prognosis of Clear Cell Renal Cell Carcinoma by Co-expression Network Analysis.
Liang Chen · Lushun Yuan · Kaiyu Qian · Guofeng Qian · Yuan Zhu · Chin-Lee Wu · Han C Dan · Yu Xiao · Xinghuan Wang
Frontiers in physiology · 2018 doi:10.3389/fphys.2018.00399
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Experiment
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Data
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Analysis
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Complete file
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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.", "claims": [{"id": "claim-method", "statement": "The “WGCNA” (Langfelder and Horvath, 2008) package in R was applied to performed co-expression network using the expression values of 2,425 DEGs from 125 tumor samples and complete clinical data (GSE73731).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29720944", "locator": "sections[5] \"Weighted gene co-expression network construction\" paragraph 2", "quote": "The “WGCNA” (Langfelder and Horvath, 2008) package in R was applied to performed co-expression network using the expression values of 2,425 DEGs from 125 tumor samples and complete clinical data (GSE73731)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Datasets GSE53757 Von Roemeling et al., 2014 and GSE36895 (Peña-Llopis et al., 2012) performed on the same platform Affymetrix Human Genome U133 Plus 2.0 Array (HG U133 Plus 2.0) were combined and analyzed to screen DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:29720944", "locator": "sections[3] \"Data collection\" paragraph 2", "quote": "Datasets GSE53757 Von Roemeling et al., 2014 and GSE36895 (Peña-Llopis et al., 2012) performed on the same platform Affymetrix Human Genome U133 Plus 2.0 Array (HG U133 Plus 2.0) were combined and analyzed to screen DEGs."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The headline result was that WGCNA-based analysis identified 29 hub genes associated with pathological stages of ccRCC, which were validated in independent datasets and associated with prognosis: “29 genes with the high connectivity in patho-module were selected as hub genes” and “Patients with higher each hub gene showed significantly shorter overall survival rate and disease-free survival rate, indicating that all hub genes could act as prognosis and recurrence biomarkers of ccRCC”. 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