Objective: To improve risk stratification for recurrence prognostication in patients with localised clear cell renal cell carcinoma (ccRCC). Patients and Methods: In all, 367 patients with non-metastatic ccRCC were included. The cohort was divided into a training and validation set. Using tissue microarrays, immunostaining was performed for 24 biomarkers representative of key pathways in ccRCC. Using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression, we identified several markers that were used to construct a risk classifier for risk of disease recurrence. Results: The median (interquartile range) follow-up was 63.5 (24.0-85.3) months. Five out of 24 markers were selected by LASSO Cox regression for the risk classifier: N-cadherin, E-cadherin, Ki67, cyclin D1 and phosphorylated eukaryotic initiation factor 4E binding protein-1 (p-4EBP1). Patients were classified as either low, intermediate or high risk of disease recurrence by tertiles of risk score. The 5-year recurrence-free survival (RFS) was 93.8%, 87.7% and 70% for patients with low-, intermediate- and high-risk scores, respectively (P < 0.001). Patients with a high marker score had worse RFS on multivariate analysis adjusted for age, gender, race and the Mayo Clinic Stage, Size, Grade, and Necrosis (SSIGN) score (hazard ratio 3.66, 95% confidence interval 1.58-8.49, P = 0.003 for high vs low marker score in the overall cohort). The five-marker classifier increased the concordance index of the clinical model in both the training and validation sets. Conclusion: We developed a five-marker-based prognostic tool that can effectively classify patients with ccRCC according to risk of disease recurrence after surgery. This tool, if prospectively validated, could provide individualised risk estimation for patients with ccRCC.
- Cell cycle
- Epithelial mesenchymal transition
- Mammalian target of rapamycin
- Renal cell carcinoma
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