Clinical value and in vitro validation of gene and molecular subtype identification in renal clear cell carcinoma

Abstract Background Clear cell renal cell carcinoma (ccRCC) constitutes a widespread and relentlessly aggressive malignancy of the urinary tract, distinguished by its high lethality and pronounced propensity for metastatic dissemination. Despite advancements in genomic therapy, the efficacy of targeted treatment for drug-resistant ccRCC remains limited, underscoring the need for robust prognostic indicators to enhance disease management. Method We utilized transcriptome data from the TCGA-KIRC and GEO cohorts to investigate gene expression disparities in ccRCC. Employing nonnegative matrix factorization, we segregated samples into specific molecular subgroups. Genes with prognostic significance were determined through both univariate and multivariate Cox proportional hazards analyses. Subsequently, we constructed a scoring algorithm to estimate patient risk and evaluated its discriminative ability via ROC curves alongside calibration charts. The model's immunotherapeutic significance was assessed through the immune phenotype score (IPS), and the sensitivity to chemotherapeutic drugs was evaluated. Result Our analysis identified seven core genes associated with ccRCC prognosis. This gene‑centred risk stratification framework bifurcated the cohort into high‑risk (HRG) and low‑risk (LRG) strata, revealing pronounced disparities in overall survival. Its prognostic fidelity was underscored by areas under the receiver operating characteristic curves of 0.88, 0.82 and 0.81 for 1, 3 and 5 - year survival projections, respectively. Functional enrichment analysis illuminated the principal biological processes and signaling pathways modulated by these genes, and the model's predictive accuracy was further confirmed through calibration curves. Conclusion Derived from ccRCC‑related gene signatures, this prognostic framework serves as a robust instrument for forecasting clinical outcomes and informing individualized therapeutic strategies. The model's predictive power, combined with its ability to assess immune cell infiltration and response to chemotherapy, offers a comprehensive approach to ccRCC management in clinical practice.