A three-gene expression signature model to predict clinical outcome of clear cell renal carcinoma.

Renal cell carcinomas (RCCs) are morphologically and genetically heterogeneous tumors and present diverse clinical courses. We developed a scoring system using levels of gene expression to predict the outcome for clear cell RCC patients. We selected differentially expressed genes from the DNA microarray data of 27 clear cell RCCs; 16 were metastasis phenotypes and 11 were not. We compared the selected gene set with previously published data and identified 33 overlapping genes closely associated with patient outcome. We selected the 12 top-ranked genes and confirmed the level of expression using quantitative reverse transcriptase PCR. Multivariate Cox analysis revealed that 3 genes-vascular cell adhesion molecule 1 (VCAM1), endothelin receptor type B (EDNRB), and regulator of G-protein signaling 5 (RGS5)-were the most tightly associated with cancer-specific survival and that higher expression of the 3 genes correlated with better outcome. A formula for an outcome predictor was generated from integration of the measurements of the expression levels of the 3 genes. Multivariate Cox models combined with a split-sample cross-validation method in a cohort of 386 clear cell RCC patients demonstrated that the derived score for outcome prediction was an independent predictor in cancer-specific survival tests. The accuracy of the prediction of cancer death after nephrectomy was improved by the inclusion of this score in receiver operating characteristic analysis from multivariate logistic regression models, suggesting that a scoring system based on the expression levels of these 3 genes is useful in the prediction of survival for patients with clear cell RCC.