INTEGRATED NETWORK ANALYSIS OF THE POTENTIAL MOLECULAR BIOMARKERS AND KEY PATHWAYS IN CLEAR RENAL CELL CARCINOMA (ccRCC)
Clear cell renal cell carcinoma (ccRCC) is the most prevailing subtype of renal cancer with the highest death rates. The objective of our study is to discover more reliable biomarkers and key pathways mostly related to ccRCC. The publicly reachable GSE168845 datasets were accessed from the Gene Expression Omnibus database. Firstly, we identified differentially expressed genes (DEGs) in ccRCC and control samples by the GEO2R tool. Second, we performed Gene Ontology (GO) and KEGG pathway analysis of determined DEGs by the DAVID database. Then, we established protein-protein interaction (PPI) networks of the identified DEGs using the STRING database and Cytoscape software. Finally, we identified the hub genes by Cytoscape software. We identified 3,935 genes as DEGs. GO function analysis in the biological process, molecular function, and cellular component category showed that DEGs were mostly involved in the inflammatory response, receptor activity and integral component of the plasma membrane, respectively. Based on KEGG pathway analysis results the identified DEGs were mostly associated with Staphylococcus aureus infection, phagosome and natural killer cell-mediated cytotoxicity. We discovered 10 hub genes that have roles in the molecular etiology of ccRCC (OXGR1, MAPK1, GNG2, LCK, ITGB2, HLA-DRB1, KIF20A, GNG10, GNB4, and HLA-DRA). In conclusion, our study discovered DEGs and associated functional terms pathways. Our results would help to reveal the pathological mechanisms of ccRCC and precise targets for the treatment of ccRCC.