A Three-Metabolic-Genes Risk Score Model Predicts Overall Survival in Clear Cell Renal Cell Carcinoma Patients
Yiqiao Zhao, Zijia Tao, Xiaonan Chen
Abstract
Metabolism alterations play crucial roles in carcinogenesis, tumor progression and prognosis in clear cell renal cell carcinoma (ccRCC). Nevertheless, a risk score (RS) model consists of metabolic genes remains unidentified. Here, we utilized gene set enrichment analysis to analyze expression data from normal group and tumor group from the cancer genome atlas and 70 KEGG metabolic pathways, then we selected seven and two pathways significantly enriched in these two groups respectively, and identified 113 genes enriched these nine pathways, we further filtered 47 prognostic related metabolic genes and used LASSO analysis to construct a three-metabolic-genes RS model composed of ALDH3A2, B3GAT3 and CPT2. We further tested the RS by mapping Kaplan-Meier plots and receiver operating characteristic (ROC) curves, the results were promising. Additionally, multivariate cox analysis showed that the RS was an independent prognostic factor, thereafter, all the independent factors and constructed a nomogram model which manifested better prediction. Finally, we validated our results by dataset from Arrayexpress and qRT-PCR. In summary, our study provided a metabolic genes RS model that might be a prognostic marker for ccRCC