Unraveling the molecular landscape of clear cell renal cell carcinoma through integrative transcriptomic analysis and validation using clinical samples

Background and aims: Clear cell renal cell carcinoma (ccRCC) is the most prevalent subtype of renal malignancies, distinguished by its aggressive nature and poor prognosis in advanced stages. Identifying prognostic biomarkers and unraveling underlying molecular events contributing to ccRCC pathogenesis is crucial for developing precise prognostic models and tailored therapeutic interventions. The objective of this research was to identify differentially expressed genes (DEGs), construct protein-protein interaction networks, pinpoint hub genes and enriched pathways, assess the prognostic relevance of these hub genes, and predict upstream regulators, thereby providing insights into the onset and progression of ccRCC. Methods: The GSE66270 dataset was reanalyzed to uncover DEGs between 14 ccRCC tumors and 14 normal tissues. Hub genes, clusters, and enriched functional categories were identified from the protein-protein interaction network. Survival analysis was performed to assess the prognostic relevance of the identified hub genes. An upstream transcription factor network was generated using the iRegulon plugin. Expression of hub genes in six cancer and six normal renal tissues was confirmed via real-time PCR. Results: A prognostic signature comprising five genes—FCGR1A, FOXM1, TOP2A, BIRC5, and CCNA2—effectively stratified ccRCC prognosis. FOXM1 was identified as the primary upstream regulator. A significant upregulation of FOXM1 was observed in renal cancer samples compared with normal renal tissues (p-value <0.001). Conclusion: These findings shed light on implicated pathways and processes, prognostic biomarkers, and the crucial role of the immune system in ccRCC pathogenesis.