The pathogenesis of kidney cancer is not fully understood, so there is an urgent need to identify new biomarkers to improve diagnosis and treatment. The study identified KIF4A, TOP2A and ASPM as novel protein biomarkers for renal cancer and explored their biological functions in the development and progression of renal cancer. A variety of bioinformatics methods were used to process and batch calibrate the transcriptome data of renal cancer. Differential gene expression analysis was performed using Limma packages, followed by functional enrichment analysis to identify biological pathways associated with kidney cancer. Construct a protein-protein interaction (PPI) network to prioritize core genes and use machine learning methods to further screen key signaling pathways. Epithelial-mesenchymal transition (EMT) marker score was used to evaluate the prognosis of renal carcinoma, and the results were validated by cell culture experiment. Integrated principal component analysis (PCA) showed significant effect of batch correction in the renal cancer cohort. Transcriptome analysis revealed dysregulation of conserved gene expression in renal carcinoma. Functional enrichment analysis showed that metabolic and immune signaling pathways play an important role in the pathogenesis of renal cancer. The integrated PPI network prioritized KIF4A, TOP2A and ASPM as key mitotic regulators and further confirmed these three as core carcinogenic drivers through machine learning. Finally, integrated prognostic analyses identified genetic features associated with EMT that are clinically significant.