Identification and validation of a two-gene metabolic signature for survival prediction in patients with kidney renal clear cell carcinoma

Background: Kidney renal clear cell carcinoma (KIRC) is one of the most common malignant tumors worldwide. Deregulated tumor cell metabolism is emerging as a common feature of tumorigenesis. The expression pattern and clinical significance of metabolism-related genes (MRGs) in KIRC remains unclear.Methods: We downloaded the RNA sequencing data and corresponding clinical information for KIRC from the Cancer Genome Atlas (TCGA) database and identified the differently expressed MRGs between tumors and normal tissues. According to the Cox regression analysis and least absolute shrinkage and selection operator (LASSO), we identified target genes for prognostic signature construction. We also analyzed the correlations of the signature risk score with clinicopathological features. The robustness of the signature was further examined by stratified survival analysis. A predictive nomogram was built for the optimal strategy to predict the survival possibility of KIRC patients. The expression levels of target genes were validated in multiple datasets. Gene set enrichment analyses (GSEA) were performed to unveil several significantly enriched pathways.Results: A total of 123 differentially expressed MRGs were identified, including 60 up-regulated genes and 63 down-regulated genes. Next, RRM2 and ALDH6A1 were identified as prognosis-related genes and used to construct a prognostic signature. The signature was proved to be an independent prognostic factor for KIRC survival by multivariable Cox regression analysis. Subgroup analysis indicated that this signature could serve as a classifier for the evaluation of low- and high-risk groups. Up regulation of RRM2 and down regulation of ALDH6A1 were associated with unfavorable prognosis in patients suffering from KIRC. Nomogram including the signature suggested some clinical net benefit for overall survival prediction. In addition, the calibration curves indicated the nomogram performed well in predicting 3‐ and 5-year OS compared with the ideal model. The expression level of RRM2 was significantly up-expressed, while ALDH6A1 were significantly down-expressed in KIRC samples compared with the normal samples in multiple datasets. Furthermore, RRM2 and ALDH6A1were significantly enriched in different pathways.Conclusion: Our study identified a two‐gene metabolic signature that had important clinical implications in KIRC prognosis prediction, which might provide potential biomarkers and targets of metabolic therapeutic relevance.