Prognostic Impact of Surgical Intervention in Clear Cell Renal Cell Carcinoma: A SEER Database Analysis with Development and Validation of a Nomogram for Surgical Patients

Background: The optimal treatment strategy for clear cell renal cell carcinoma (ccRCC) remains controversial, particularly due to the lack of high-quality evidence regarding the survival benefits of non-surgical approaches. Methods: The data for this study were extracted from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute, with propensity score matching (PSM) applied to balance baseline characteristic disparities. Overall survival was compared using survival curves, and subgroup heterogeneity analysis was conducted. Prognostic factors were identified for patients who underwent surgery, and based on these parameters, a predictive nomogram for overall survival (OS) was constructed, followed by cross-database validation with The Cancer Genome Atlas (TCGA) database. Results: Survival analysis revealed significantly better OS in the surgical group compared to the non-surgical group (HR=0.405, 95%CI: 0.382-0.429, P<0.001), with consistent survival advantages observed across all predefined subgroups (all HR<1, P<0.05). Univariate and multivariate analysis identified age, sex, tumor stage, T stage, N stage, M stage, and differentiation grade as independent prognostic factors for OS in surgical ccRCC patients. The nomogram model incorporating these variables demonstrated excellent discriminative ability, calibration, and clinical utility in the training, internal validation, and external test cohort. Conclusion: Surgical intervention significantly improves survival outcomes in patients with ccRCC. The dynamic prediction model incorporating seven independent prognostic factors overcomes the static limitations of conventional TNM staging systems and demonstrates superior predictive performance in cross-database validation. This model provides an improved tool for guiding clinical decision-making, optimizing follow-up strategies, and selecting appropriate candidates for clinical trials in ccRCC management.