Gene profiling and clinicopathological features for prognostic modeling of recurrence in non-metastatic clear-cell renal cell carcinoma.

Background: Accurate risk stratification of renal cell carcinoma (RCC) is critical for selecting the most appropriate treatment options. Existing prognostic systems, which incorporate various clinical and pathological parameters, have limitations in terms of accuracy. However, it remains unclear whether integrating molecular data with clinicopathological features can enhance the identification of high-risk tumors. The objective of this study was to establish a model to predict RCC recurrence by integrating molecular data with clinicopathological features and to evaluate circulating tumor DNA (ctDNA) as a non-invasive prognostic marker. Methods: Next-generation sequencing (NGS) was performed on 73 RCC patients, including 54 with clear-cell RCC (ccRCC). A prognostic model for disease-free survival (DFS) in non-metastatic ccRCC (NMCCRCC) was constructed and validated with two external datasets. The prognostic potential of ctDNA was assessed by its detection rates, mutation concordance with tumor tissue DNA, and association with clinical outcomes. Results: Frequently altered genes in ccRCC included Conclusions: Our prognostic model, factoring in T stage and genetic mutations in BAP1 and SETD2, effectively predicted recurrence in NMCCRCC patients. The potential of ctDNA as a non-invasive prognostic biomarker was underscored by its high detection rates and mutation concordance.