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

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: ) effectively stratified patients into high- and low-risk groups with distinct survival outcomes. High-risk patients exhibited enrichment of extracellular matrix (ECM) and inflammatory pathways, whereas low-risk patients were associated with mitochondrial metabolism. Immune profiling revealed increased fibroblasts and myeloid cells in the high-risk group. Clustering based on DRRGs identified two molecular subtypes with different metabolic and immune features. High-risk tumors exhibited elevated tumor mutational burden (TMB) and microsatellite instability-high (MSI-H) frequency, while risk scores were inversely associated with stemness. IPS analysis further indicated that low-risk patients may derive greater benefit from CTLA-4 blockade. In metastasis prediction (GSE41258), the XGBoost model achieved an area under the receiver operating characteristic curve (AUC) of 0.855, with Matrix Gla Protein (MGP) identified as a key contributor via SHAP analysis. 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.