Identifying biomarkers of papillary renal cell carcinoma associated with pathological stage by weighted gene co-expression network analysis
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Zhongshi He1,2,3, Min Sun1,3,4, Yuan Ke1,2,3, Rongjie Lin1,2,3, Youde Xiao1,2,3, Shuliang Zhou1,2,3, Hong Zhao1,2,3, Yan Wang1,2,3, Fuxiang Zhou2,3, Yunfeng Zhou2,3
1Hubei Cancer Clinical Study Center, Hubei Key Laboratory of Tumor Biological Behaviors, Wuhan, China
2Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
3Hubei Cancer Clinical Study Center, Zhongnan Hospital of Wuhan University, Wuhan, China
4Department of Oncology, Zhongnan Hospital of Wuhan University, Wuhan, China
Fuxiang Zhou, email: email@example.com
Yunfeng Zhou, email: firstname.lastname@example.org
Keywords: papillary renal cell carcinoma (PRCC), the cancer genome atlas (TCGA), weighted gene co-expression network analysis (WGCNA), survival prognosis, pathological stage
Received: November 02, 2016 Accepted: February 20, 2017 Published: March 02, 2017
Although papillary renal cell carcinoma (PRCC) accounts for 10%–15% of renal cell carcinoma (RCC), no predictive molecular biomarker is currently applicable to guiding disease stage of PRCC patients. The mRNASeq data of PRCC and adjacent normal tissue in The Cancer Genome Atlas was analyzed to identify 1148 differentially expressed genes, on which weighted gene co-expression network analysis was performed. Then 11 co-expressed gene modules were identified. The highest association was found between blue module and pathological stage (r = 0.45) by Pearson’s correlation analysis. Functional enrichment analysis revealed that biological processes of blue module focused on nuclear division, cell cycle phase, and spindle (all P < 1e-10). All 40 hub genes in blue module can distinguish localized (pathological stage I, II) from non-localized (pathological stage III, IV) PRCC (P < 0.01). A good molecular biomarker for pathological stage of RCC must be a prognostic gene in clinical practice. Survival analysis was performed to reversely validate if hub genes were associated with pathological stage. Survival analysis unveiled that all hub genes were associated with patient prognosis (P < 0.01).The validation cohort GSE2748 verified that 30 hub genes can differentiate localized from non-localized PRCC (P < 0.01), and 18 hub genes are prognosis-associated (P < 0.01).
ROC curve indicated that the 17 hub genes exhibited excellent diagnostic efficiency for localized and non-localized PRCC (AUC > 0.7). These hub genes may serve as a biomarker and help to distinguish different pathological stages for PRCC patients.
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