Abstract
BACKGROUND: Colon cancer is one of the leading causes of cancer-related mortality worldwide, and most patients are diagnosed at advanced stages owing to the lack of reliable biomarkers. Metabolic reprogramming, a hallmark of cancer progression, involves cofactors and vitamin metabolism, which regulate enzymatic activity, epigenetic modifications, and the tumor immune microenvironment. However, their prognostic value remains unclear. This study aims to construct and validate a novel prognostic model for colon cancer based on cofactor and vitamin metabolism-related genes (CVMRGs). METHODS: Transcriptomic data from 454 colon adenocarcinoma (COAD) tumors [The Cancer Genome Atlas (TCGA)] and 562 validation samples [Gene Expression Omnibus (GEO); GSE39582] were analyzed. A total of 214 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 least absolute shrinkage and selection operator (LASSO)-Cox regression. Model validation encompassed Kaplan-Meier survival analysis, correlation analysis with consensus molecular subtypes (CMS) using the "CMScaller" package, time-dependent receiver operating characteristic (ROC) curves, immune microenvironment profiling [Tumor Immune Dysfunction and Exclusion (TIDE), Estimation of Stromal and Immune Cells in Malignant Tumors using Expression data (ESTIMATE), Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT)] , and drug sensitivity prediction. RESULTS: The risk score independently predicted overall survival (OS) [1-, 3-, and 5-year area under the curve (AUC): 0.776, 0.771, 0.759, respectively] and correlated significantly with advanced tumor-node-metastasis (TNM) stages (P<0.001). Notably, the risk score was significantly higher in CMS4 (mesenchymal type, worst prognosis) than in CMS1 (MSI immune type), CMS2 (canonical type), and CMS3 (metabolic type) (P=0.0001, 0.0003, and 4.8e-08, respectively), indicating the model captures features linked to aggressive molecular subtypes. High-risk patients exhibited enriched epithelial-mesenchymal transition (EMT) 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=0.007). The robustness of the model was confirmed in the GSE39582 cohort. CONCLUSIONS: 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.