Abstract
OBJECTIVE: This study aimed to develop and validate a nomogram for predicting recurrence-free survival (RFS) in clinical T1 (cT1) clear cell renal cell carcinoma (ccRCC) following nephrectomy. Additionally, it explored transcriptional profiles across distinct risk groups. METHODS: Data from 2,492 cT1 ccRCC patients who underwent nephrectomy at The Second Hospital of Tianjin Medical University were analyzed. Univariate and multivariate Cox proportional hazards regression analyses were conducted to identify independent predictors of RFS. A nomogram was constructed and validated using a training cohort (n = 1744) and an internal validation cohort (n = 748). Model performance was evaluated using the concordance index (C-index), calibration plots, receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and Kaplan-Meier survival curves. An external validation was performed using The Cancer Genome Atlas (TCGA) ccRCC dataset. Furthermore, Cox-Lasso regression analysis was applied to identify risk-associated genes in the high-risk group. RESULTS: Age, surgical margin status, Fuhrman grade, and pT3a upstage were identified as independent predictors. The areas under the ROC curve (AUC) for 3-year and 5-year RFS were 0.748 and 0.762 in the training cohort; 0.777 and 0.776 in the internal validation cohort; and 0.706 and 0.746 in the external validation cohort. Kaplan-Meier analysis showed significant differences in RFS between low- and high-risk groups across all cohorts (p < 0.0001, p < 0.0001, p = 0.0010, respectively). Nine genes, including MMP13, ITPKA, ATG9B, and CACNA1B, were identified as poor prognosis markers. CONCLUSIONS: We developed and validated a robust nomogram for predicting RFS in cT1 ccRCC patients after nephrectomy, offering valuable insights for individualized patient management.