Background: Management of patients with localized or locally advanced renal cell cancer (RCC) involves surgical resection. However, 20-40% of all localized kidney cancer patients experience a recurrence. Adjuvant treatment in high-risk kidney cancer has not proven to successfully improve overall survival (OS) and the only drug approved so far in this setting is vascular endothelial growth factor (VEGF) inhibitor, sunitinib. As multiple trials are evaluating drugs including immune checkpoint inhibitors in patients with high risk localized clear cell RCC, there is an urgent need to identify biomarkers to help with therapeutic decisions as well as for risk stratification. Here we present analysis pertaining to genes involved in metabolic reprogramming in kidney cancer. Methods: A deep transcriptomic analysis of patients with stage III ccRCC in the TCGA KIRC Firehose Legacy cohort was undertaken. Caucasian males with stage III ccRCC in the TCGA cohort for whom recurrence data was available with a minimum follow-up of 2 years were identified for the analysis. Expression profiles of differentially expressed genes were clustered using the Bayesian infinite mixture model and samples clustered using average linkage hierarchical clustering based on pairwise Pearson’s correlations as the measure of similarity. Enrichment analysis of up- and down-regulated genes was performed using logistic regression. R package was used to generate Kaplan-Meier curves and assess statistical significance of differences in overall and disease-free survival using log-rank test. Results: The cohort evaluated for this study included Caucasian male patients that remained disease free for at least 24 months after surgery (n = 22) and patients whose cancer recurred within 24 months (n = 20). We identified metabolic genes, encoding subunits of mitochondrial electron transport chain as well as malate aspartate shuttle (MAS), where loss of coordinated co-expression between these genes identified patients at risk of recurrence after surgery. The gene signature stratified the 42 patients into three subtypes: significantly enriched for a) recurrence (subtype 1), for b) disease free status (subtype 2) and b) intermediate (subtype 3). Work is underway to combine the use of individual gene expression and ratios of gene expression within the signature to optimize prognostic biomarkers for localized ccRCC. Conclusions: In this analysis, we have identified a novel metabolic gene signature which can help identify patients with localized ccRCC at high risk of recurrence after surgery to guide aggressive therapy. These findings require further validation in tumors collected from patients with stage III ccRCC.