Introduction: Clear cell renal cell carcinoma is the most frequent subtype of kidney cancer. The need for beneficial biomarkers has become more compelling due to the high mortality of this cancer. We aimed to determine potential biomarkers associated with ccRCC for early diagnosis and treatment.
Methods: The TCGA-KIRC dataset including 72 normal and 542 cancer samples were obtained using TCGAbiolinks package in R programming language and, furthermore, only protein-coding genes were selected by biomaRt package. Expression matrix was normalized through TMM method from edgeR package, and consequently differentially expressed genes (DEGs) with |log2FC| > 1.5 and adj P-value < 0.01 were identified. The Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed utilizing enrichment tool in Enrichr website, and STRING database was used to establish protein-protein interaction (PPI) network diagram. PPI network was visualized by Cytoscape software and hub genes were calculated by the cytoHubba plugin. Finally, the Gene Expression Profiling Interactive Analysis (GEPIA) database was utilized to perform prognostic value and survival analysis of the hub genes.
Results: Among all 14,656 protein-coding genes, 1,227 DEGs were identified, consisting of 681 down-regulated and 546 up-regulated genes. The results of KEGG pathway analysis demonstrated that the cell adhesion molecules pathway was significantly enriched. In addition, based on GO annotation, DEGs were mainly involved in molecular function of urate transmembrane transporter activity (GO:0015143), cellular component of collagen-containing extracellular matrix (GO:0062023) and biological process of extracellular matrix organization (GO: 0030198). Ultimately, 15 genes with highest maximal clique centrality (MCC) value were regarded as hub genes and according to the survival analysis only 11 of them (UBE2C, HJURP, AURKB, KIF20A, PTTG1, BIRC5, TOP2A, BUB1, CCNA2, CEP55 and TPX2) which had log-rank test (P<0.01) and HR> 1 were significantly associated with poor prognosis.
Conclusion: The present study illustrated that these 11 detected hub genes: UBE2C, HJURP, AURKB, KIF20A, PTTG1, BIRC5, TOP2A, BUB1, CCNA2, CEP55 and TPX2 may be considered promising biomarkers to improve the poor prognosis of this lethal disease through early diagnosis.