A Novel Prognostic Model for Colon Adenocarcinoma Based on Cofactor and Vitamin Metabolism-Related Genes

Purpose Colon cancer is one of the leading causes of cancer-related mortality worldwide, with most patients diagnosed at advanced stages due to the lack of reliable biomarkers. Metabolic reprogramming, a hallmark of cancer progression, involves cofactor and vitamin metabolism, which regulates enzymatic activity, epigenetic modifications, and the tumor immune microenvironment. This study aims to construct a novel risk prediction model for prognostic evaluation in colon cancer patients based on genes associated with cofactor and vitamin metabolism. Methods Transcriptomic data from 454 colon adenocarcinoma tumors (The Cancer Genome Atlas, TCGA) and 562 validation samples (Gene Expression Omnibus, GSE39582) were analyzed. A total of 214 cofactor and vitamin metabolism-related genes (CVMRGs) were screened using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotations. Differential expression analysis and univariate Cox regression identified 10 prognosis-associated genes. A 6-gene risk model (DLAT, TH, AK7, ALDH2, ALAD, CYP26A1) was constructed via LASSO-Cox regression. Model validation encompassed Kaplan-Meier survival analysis, time-dependent receiver operating characteristic (ROC) curves, immune microenvironment profiling (TIDE, ESTIMATE, CIBERSORT), and drug sensitivity prediction. Results The risk score independently predicted overall survival (OS) (1-, 3-, and 5-year AUC: 0.776, 0.771, 0.759, respectively) and correlated significantly with advanced TNM stages (P < 0.001). High-risk patients exhibited enriched epithelial-mesenchymal transition pathways and immunosuppressive microenvironments (elevated cancer-associated fibroblasts [CAFs], TIDE scores), while low-risk patients demonstrated activation of oxidative phosphorylation. Drug sensitivity analysis revealed that the high-risk group was more sensitive to fluorouracil and gemcitabine (P < 0.001), whereas the low-risk group showed better responses to regorafenib (P = 0.0074). The robustness of the model was confirmed in the GSE39582 cohort. Conclusion This study establishes a novel prognostic model for COAD based on cofactor and vitamin metabolism, enabling precise survival prediction and guiding personalized therapeutic strategies. The model underscores the interplay between metabolic-immune crosstalk and chemotherapy response heterogeneity, providing a framework for developing targeted metabolic therapies combined with immune modulation.