Human clear cell renal cell carcinoma (ccRCC) is the most common and frequently occurring histological subtype of RCC.Unlike other carcinomas, candidate predictive biomarkers for this type are in need to explore the molecular mechanism of ccRCC andidentify candidate target genes for improving disease management. For this, we chose case–control-based studies from the Gene ExpressionOmnibus and subjected the gene expression microarray data to combined effect size meta-analysis for identifying shared genes signature.Further, we constructed a subnetwork of these gene signatures and evaluated topological parameters during the gene deletion analysis to getto the central hub genes, as they form the backbone of the network and its integrity. Parallelly, we carried out functional enrichment analysisusing gene ontology and Elsevier disease pathway collection. We also performed microRNAs target gene analysis and constructed aregulatory network. We identified a total of 577 differentially expressed genes (DEGs), where 146 overexpressed and 431 underexpressedwith a significant threshold of adjusted P<0.05. Enrichment analysis of these DEGs’ functions showed a relation to metabolic andcellular pathways like metabolic reprogramming in cancer, proteins with altered expression in cancer metabolic reprogramming, andglycolysis activation in cancer (Warburg effect). Our analysis revealed the potential role of PDHB and ATP5C1 in ccRCC by alteringmetabolic pathways and amyloid beta precursor protein (APP) role in altering cell-cycle growth for the tumour progression in ccRCCconditions. Identification of these candidate predictive genes paves the way for the development of biomarker-based methods for thiscarcinoma.