Identification of Molecular Signatures and Prognostic Markers in Clear Cell Renal Cell Carcinoma through Advanced Bioinformatics and Systems Biology

This study aimed to find potential genes that could serve as biomarkers for the diagnosis, prognosis, or targeted therapy of clear cell renal cell carcinoma (ccRCC). Two expression profiles, GSE36895 and GSE53757, were taken from Gene Expression Omnibus. Differentially regulated genes (DRGs) between clear cell renal cell carcinoma (ccRCC) and normal tissues were identified using GEO2R. The analysis yielded 409 upregulated and 125 downregulated genes (adjusted p-value < 0.05, |log2FC| ± 2). Functional annotation and KEGG pathway enrichment, performed using the DAVID database. The results indicated significant involvement of these genes in pathways closely related to the pathophysiology of ccRCC. These include the PI3K-Akt signaling, metabolic processes, and cell cycle regulation. Protein-protein interaction (PPI) network analysis was constructed using GeneMANIA and visualized with Cytoscape. Module analysis was performed with the MCODE plugin. Based on network connectivity and biological relevance, twelve hub genes were identified. These hub genes were further analyzed for expression and prognostic relevance using UALCAN and GEPIA, both based on TCGA data. Prognostic analysis revealed that six genes, SLC22A2, TMEM174, ANGPTL3, SMIM24, AOX1, and FYB2, were significantly associated with better overall survival in ccRCC patients (log-rank p < 0.05; HR<1). In contrast, high expression of TMEM45A was linked to poor prognosis (log-rank p < 0.05; HR >1). Overall, the study findings highlight novel gene candidates with potential clinical significance. The identified hub genes may serve as prognostic indicators and possible therapeutic targets, contributing to improved clinical management of ccRCC in the future.