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
- 1 Department of Surgery, Pudong Branch of Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200126, China
- 2 Cancer Institute, Fudan University Shanghai Cancer Center, Shanghai 200032, P.R. China
- 3 Department of Urology, Fudan University Shanghai Cancer Center, Shanghai 200032, P.R. China
- 4 Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, P.R. China
Received: September 10, 2019 Accepted: November 19, 2019 Published: December 18, 2019https://doi.org/10.18632/aging.102540
How to Cite
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.
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.