Evidence map›Paper›PMID 41378020›Full record

ArticleTranslational cancer research2025

Dietary restriction-responsive genes define prognostic subtypes and predict metastasis in colorectal cancer.

Qibin Li, Zengtao Wang, Jinyi Long, Ziyang Xu, Jie Zhang

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Article in Translational cancer research, 2025. 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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5 authors.

Qibin Li *Department of Anesthesiology, Huashan Hospital-North Fudan University, Shanghai, China.
Zengtao Wang *Department of Anesthesiology, Huashan Hospital-North Fudan University, Shanghai, China.
Jinyi Long *Department of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ziyang XuDepartment of Surgery, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jie ZhangDepartment of Anesthesiology, Huashan Hospital-North Fudan University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Colorectal cancer (CRC) is a major cause of cancer-related death, with a poor prognosis often due to metastasis and recurrence. Dietary restriction (DR) is known to delay tumor progression and extend lifespan, but the roles of dietary restriction-responsive genes (DRRGs) in CRC remain unclear. This study aimed to identify prognostic DRRGs and explore their associations with tumor behavior and immune features. Methods: Transcriptomic data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed alongside 276 DRRGs from the GenDR database. Differentially expressed DRRGs were identified, followed by univariate Cox regression to assess prognostic relevance. A least absolute shrinkage and selection operator (LASSO)-Cox model was used to construct a prognostic signature, which was validated in external cohorts. Immune cell infiltration, functional enrichment, and unsupervised clustering were performed to evaluate the biological roles of DRRGs. Associations of the risk score with clinicopathological features, genomic alterations, and immunotherapy response (IPS) were further evaluated. Machine learning (ML) models were built to predict metastasis and recurrence using Shapley Additive exPlanations (SHAP) analysis. Results: A total of 11 DRRGs were found to be significantly associated with CRC prognosis. A four-gene signature ( Conclusions: We established a DRRG-based prognostic model for CRC and uncovered their links to metabolic regulation, immune infiltration, and metastasis. These findings highlight DRRGs as potential biomarkers and therapeutic targets and suggest that DR-mimicking strategies may benefit CRC management.

Indexed as

colorectal cancer (CRC)Dietary restriction (DR)metastasis predictionprognostic gene signatureShapley Additive exPlanations analysis (SHAP analysis)

Identifiers

PMID41378020
PMCPMC12686144

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