Identification of Metabolic-Associated Genes for the Prediction of Colon and Rectal Adenocarcinoma

Background and Aim: Uncontrolled proliferation is the most prominent biological feature of tumors. In order to rapidly proliferate, tumor cells regulate their metabolic behavior by controlling the expression of metabolism-related genes (MRGs) to maximize the utilization of available nutrients. In this study, we aimed to construct prognosis models for colorectal adenocarcinoma (COAD) and rectum adenocarcinoma (READ) using MRGs to predict the prognoses of patients. Methods: We first acquired the gene expression profiles of COAD and READ from the TCGA database, and then utilized univariate Cox analysis, Lasso regression, and multivariable Cox analysis to identify the MRGs for risk models. Results: Eight genes (CPT1C, PLCB2, PLA2G2D, GAUL ENPP2, PIP4K2B, GPX3, and GSR) in the colon cancer risk model and six genes (TDO2, PKLR, GAUL EARS2, ACO1, and WAS) in the rectal cancer risk model were identified successfully. Multivariate Cox analysis indicated that these two models could accurately and independently predict overall survival (OS) for patients with COAD or READ. Furthermore, functional enrichment analysis was used to identify the metabolism pathway of MRGs in the risk models and analyzed these genes comprehensively. Then, we verified the prognosis model in independent COAD cohorts (GSE17538) and detected the correlations of the protein expression levels of GSR and ENPP2 with prognosis for COAD or READ. Conclusion: In this study, 14 MRGs were identified as potential prognostic biomarkers and therapeutic targets for colorectal cancer.

Automated summary

By constructing prognosis models for colorectal adenocarcinoma and rectum adenocarcinoma using metabolism-related genes, this study successfully predicted patient survival and identified 14 potential prognostic biomarkers and therapeutic targets for colorectal cancer.