Identification of biomarkers and potential molecular mechanisms of clear cell renal cell carcinoma
Abstract:
Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cancer in adults. The aim of this study is to identify the biomarkers and potential molecular mechanisms of ccRCC. Three gene expression profiles and two miRNA expression profiles were downloaded from GEO database. A total of 330 up-regulated (differentially expressed genes) DEGs, 545 down-regulated DEGs, 26 up-regulated (differentially expressed miRNAs) DEMs and 11 down-regulated DEMs were identified by GEO2R. The gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by KOBAS software. The results showed that GO terms of the up-regulated DEGs were mostly enriched in response to stimulus at BP level, cell periphery at CC level and binding at MF level, while the GO terms of down-regulated DEGs were enriched in single-organism process at BP level, extracellular exosome at CC level and catalytic activity at MF level. As for KEGG pathways, HIF-1 signaling pathway, focal adhesion, PI3K-Akt signaling pathway and metabolic pathways were significantly enriched. Then, protein-protein interaction (PPI) network and miRNA-gene network were constructed and analyzed by Cytoscape. A total of eight DEGs were identified as biomarkers, including VEGFA, PPARA, CCND1, FLT1, CXCL12, FN1, DCN and ERBB4. Expression validation and survival analysis were performed by GEPIA and OncoLnc, respectively. Four biomarkers were verified by quantitative real-time PCR (qPCR) in 786-O cell line and HK-2 cell line. All four genes had the same expression trend as predicted. Our study provides a series of biomarkers and molecular mechanisms for the deeper research of ccRCC.
Received date: 05/11/2017
Accepted date: 07/19/2017
Ahead of print publish date: 03/13/2018
Issue: 2/2018
Volume: 65
Pages: 242 — 252
Keywords: clear cell renal cell carcinoma, differentially expressed gene, protein-protein interaction network, miRNA, survival analysis
DOI: 10.4149/neo_2018_170511N342