Identification of biomarkers of clear cell renal cell carcinoma by bioinformatics analysis.

Abstract

Clear cell renal cell carcinoma (ccRCC) is the most common subtype among renal cancer, and more and more researches find that the occurrence of ccRCC is associated with genetic changes, but the molecular mechanism still remains unclear. The present study aimed to identify aggregation trend of differentially expressed genes (DEGs) in ccRCC, which would be beneficial to the treatment of ccRCC and provide research ideas using a series of bioinformatics approach. Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genomes (KEGG) analysis were used to get the enrichment trend of DEGs of GSE53757 and GSE16449. Draw Venn Diagram was applied for co-expression of DEGs. Cytoscape with the Retrieval of Interacting Gene (STRING) datasets and Molecular Complex Detection (MCODE) were performed protein-protein interaction (PPI) of DEGs. The Kaplan–Meier Plotter analysis of top 15 upregulated and top 15 downregulated were selected in Gene Expression Profiling Interactive Analysis (GEPIA). Then, the expression level of hub genes between normal renal tissue and different pathological stages of ccRCC tissue, which significantly correlated with overall survival in ccRCC patients, were also analyzed by Ualcan based on The Cancer Genome Atlas (TCGA) database. In this study, we got 167 co-expression DEGs, including 72 upregulated DEGs and 95 downregulated DEGs. We identified 11 hub genes had significantly correlated with overall survival in ccRCC patients. Among them, KIF23, APLN, ADCY1, GREB1, TLR4, IRF8, CXCL1, CXCL2, deserved our attention.