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Research Paper|Volume 11, Issue 24|pp 12057—12079

Screening, identification and validation of CCND1 and PECAM1/CD31 for predicting prognosis in renal cell carcinoma patients

Jian-Feng Yang1, Shen-Nan Shi2,4, Wen-Hao Xu3,4, Yun-Hua Qiu1, Jin-Zhou Zheng1, Kui Yu1, Xiao-Yun Song1, Feng Li1, Yu Wang1, Rui Wang1, Yuan-Yuan Qu3,4, Hai-Liang Zhang3,4, Xi-Qiu Zhou1
  • 1Department of Surgery, Pudong Branch of Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200126, China
  • 2Cancer Institute, Fudan University Shanghai Cancer Center, Shanghai 200032, P.R. China
  • 3Department of Urology, Fudan University Shanghai Cancer Center, Shanghai 200032, P.R. China
  • 4Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, P.R. China
* Equal contribution
Received: September 10, 2019Accepted: November 19, 2019Published: December 18, 2019
https://doi.org/10.18632/aging.102540

Copyright © 2019 Yang et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Clear cell renal cell carcinoma (ccRCC) is one of the most common cancers worldwide. Despite intense efforts to elucidate its pathogenesis, the molecular mechanisms and genetic characteristics of this cancer remain unknown. In this study, three expression profile data sets (GSE15641, GSE16441 and GSE66270) were integrated to identify candidate genes that could elucidate functional pathways in ccRCC. Expression data from 63 ccRCC tumors and 54 normal samples were pooled and analyzed. The GSE profiles shared 379 differentially expressed genes (DEGs), including 249 upregulated genes, and 130 downregulated genes. A protein-protein interaction network (PPI) was constructed and analyzed using STRING and Cytoscape. Functional and signaling pathways of the shared DEGs with significant p values were identified. Kaplan-Meier plots of integrated expression scores were used to analyze survival outcomes. These suggested that FN1, ICAM1, CXCR4, TYROBP, EGF, CAV1, CCND1 and PECAM1/CD31 were independent prognostic factors in ccRCC. Finally, to investigate early events in renal cancer, we screened for the hub genes CCND1 and PECAM1/CD31. In summary, integrated bioinformatics analysis identified candidate DEGs and pathways in ccRCC that could improve our understanding of the causes and underlying molecular events of ccRCC. These candidate genes and pathways could be therapeutic targets for ccRCC.