Evidence map›Paper›PMID 41674972›Full record

ArticleTranslational cancer research2026

A novel prognostic model for colon adenocarcinoma based on cofactor and vitamin metabolism-related genes.

Qinglin Yang, Zhouyuan Du, Haixin Yu, Tao Liu

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Article in Translational cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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4 authors.

Qinglin YangDepartment of Digestive Surgical Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.ORCID https://orcid.org/0009-0007-2305-4203
Zhouyuan DuDepartment of Digestive Surgical Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Haixin YuDepartment of Digestive Surgical Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Tao LiuDepartment of Digestive Surgical Oncology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 ( 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.

Indexed as

cofactor and vitamin metabolism-related genes (CVMRGs)Colon adenocarcinoma (COAD)drug sensitivityprognostic modeltumor immune microenvironment

Identifiers

PMID41674972
PMCPMC12885891

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.