Prognostic value of a gene signature in clear cell renal cell carcinoma

Renal cancer is acommon urogenital system malignance. Novel biomarkers could provide more and more critical information ontumor features and patients' prognosis. Here, we performed an integrated analysis on the discovery set and established a three-gene signature to predict the prognosis for clear cell renal cell carcinoma (ccRCC). By constructing a LASSO Cox regression model, a 3-messenger RNA (3-mRNA) signature was identified. Based on the 3-mRNA signature, we divided patients into high- and low-risk groups, and validated thisby using three other data sets. In the discovery set, this signature could successfully distinguish between the high- and low-risk patients (hazard ratio (HR), 2.152; 95% confidence interval (CI),1.509-3.069; p<0.0001). Analysis ofinternal and two external validation sets yielded consistent results (internal: HR, 2.824; 95% CI, 1.601-4.98; p<0.001; GSE29609: HR, 3.002; 95% CI, 1.113-8.094; p=0.031; E-MTAB-3267: HR, 2.357; 95% CI, 1.243-4.468; p=0.006). Time-dependent receiver operating characteristic (ROC) analysis indicated that the area under the ROC curve at 5 years was 0.66 both in the discovery and internal validation set, while the two external validation sets also suggested good performance of the 3-mRNA signature. Besides that, a nomogram was built and the calibration plots and decision curve analysis indicated the good performance and clinical utility of the nomogram. In conclusion, this 3-mRNA classifier proved to be a useful tool for prognostic evaluation and could facilitate personalized management of ccRCC patients.