Identification of a Four-Gene Metabolic Signature to Evaluate the Prognosis of Colon Adenocarcinoma Patients.

Background: Colon adenocarcinoma (COAD) is a highly heterogeneous disease, thus making prognostic predictions uniquely challenging. Metabolic reprogramming is emerging as a novel cancer hallmark that may serve as the basis for more effective prognosis strategies. Methods: The mRNA expression profiles and relevant clinical information of COAD patients were downloaded from public resources. The least absolute shrinkage and selection operator (LASSO) Cox regression model was exploited to establish a prognostic model, which was performed to gain risk scores for multiple genes in The Cancer Genome Atlas (TCGA) COAD patients and validated in GSE39582 cohort. A forest plot and nomogram were constructed to visualize the data. The clinical nomogram was calibrated using a calibration curve coupled with decision curve analysis (DCA). The association between the model genes' expression and six types of infiltrating immunocytes was evaluated. Apoptosis, cell cycle assays and cell transfection experiments were performed. Results: Univariate Cox regression analysis results indicated that ten differentially expressed genes (DEGs) were related with disease-free survival (DFS) ( Conclusion: This research reveals that a novel metabolic gene signature could be used to evaluate the prognosis of COAD, and targeting metabolic pathways may serve as a therapeutic alternative.