Purpose: Aim of this study was to develop a multi-gene signature to help better predict prognosis for stage III renal cell carcinoma (RCC) patients. Methods: Fourteen pairs of stage III tumor and normal tissues mRNA expression data from GSE53757 and 16 pairs mRNA expression data from TCGA clear cell RCC database were used to analyze differentially expressed genes between tumor and normal tissues. Common different expressed genes in both datasets were used for further modeling. Lasso Cox regression analysis was performed to select and build prognostic multi-gene signature in TCGA stage III kidney cancer patients (N = 122). Then, the multi-gene signature was validated in stage III renal cancer cases in Fudan University Shanghai Cancer Center (N = 77). C-index and time-dependent ROC were used to test the efficiency of this signature in predicting overall survival. Results: In total, 1,370 common different expressed genes were found between tumor and normal tissues in both datasets. After Lasso Cox modeling, nine mRNAs were finally identified to build a classifier. Using this classifier, we could classify stage III clear cell RCC patients into high-risk group and low-risk group. Prognosis was significantly different between these groups in discovery TCGA cohort, validation FUSCC cohort and entire set (All P < 0.001). Multivariate cox regression in entire set (N = 199) revealed that risk group classified by 9-gene signature, age of diagnosis, pN stage and ISUP grade were independent prognostic factor of overall survival in stage III kidney cancer patients. Conclusion: We developed a robust multi-gene classifier that can effectively classify stage III RCC patients into groups with low and high risk of poor prognosis. This signature may help select high-risk patients who require more aggressive adjuvant target therapy or immune therapy.