A Cori cycle-related gene signature predicts prognosis, immune microenvironment, and drug response in breast cancer.

[BACKGROUND] Breast cancer remains the most common malignancy among women worldwide. Metabolic reprogramming, particularly involving the Cori cycle, plays a crucial role in tumor progression and therapy resistance. However, the prognostic and immunological implications of Cori cycle-related genes (CCRGs) in breast cancer remain underexplored. This study aimed to integrate multi-omics data and machine learning to construct a CCRG-based prognostic signature and evaluate its predictive performance and clinical relevance in breast cancer.

[METHODS] We integrated multi-omics data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A machine learning approach was employed to construct a prognostic signature based on CCRGs. The model was validated across independent cohorts. Immune infiltration, drug sensitivity, somatic mutations, and functional enrichment analyses were performed to elucidate the biological and clinical relevance of the signature. Single-cell and pan-cancer analyses were conducted to assess gene expression and functional associations at cellular and cross-cancer levels.

[RESULTS] A three-gene signature (, , ) was identified and used to stratify patients into high- and low-risk groups with distinct survival outcomes. The risk score (RS) was significantly associated with advanced clinicopathological features, immunosuppressive microenvironments, and altered drug sensitivity. Enrichment analyses revealed activation of glycolysis, cell cycle, and PI3K-AKT pathways in high-risk patients. The signature also correlated with tumor mutational burden (TMB) and specific mutational patterns. Pan-cancer analysis confirmed the broad relevance of CCRGs across multiple cancer types.

[CONCLUSIONS] We developed and validated a robust CCRG signature that effectively predicts prognosis, immune contexture, and therapeutic response in breast cancer. This signature offers novel insights into metabolic immunosuppression and provides a potential tool for risk stratification and personalized treatment strategies.