Functional genomics integration of glycolysis-related gene networks reveals prognostic biomarkers and immune microenvironment regulation in breast cancer.

Breast cancer (BC) is the most common malignancy among women worldwide, with metabolic reprogramming, particularly alterations in glycolytic pathways, playing a crucial role in its development and progression. This study aimed to perform an integrative multi-omics analysis of glycolysis-related genes (GRGs) expression profiles in breast cancer, construct a prognostic prediction model, explore the regulatory mechanisms of glycolytic reprogramming on the tumor immune microenvironment, and identify potential therapeutic targets. We integrated multi-omics data from TCGA and METABRIC databases with single-cell RNA sequencing data from GEO. Key GRGs were identified through differential expression analysis and WGCNA. Patients were stratified via consensus clustering, and a prognostic model was developed using Cox and LASSO regression analyses. Immune microenvironment features were characterized using ESTIMATE and CIBERSORT algorithms. Single-cell sequencing analysis revealed glycolytic profiles across different cell types and intercellular communication networks. Mendelian randomization was performed to establish causal relationships between GRGs and BC, using eQTL data as instrumental variables with multiple estimation methods (IVW, weighted median, MR-Egger) and rigorous validity assessments. Potential therapeutic agents were identified through molecular docking, and key gene expression was validated by PCR. We established a prognostic risk model based on GRGs that demonstrated good predictive value in both TCGA and METABRIC cohorts. Immune microenvironment analysis revealed increased eosinophils and M2 macrophages in the high-risk group, while dendritic cells and effector T cells were enriched in the low-risk group. Single-cell analysis confirmed higher glycolytic activity in myeloid cells and T cells within tumor tissues. Cell communication network analysis demonstrated that myeloid cells primarily interact with other cells through MHC-II, MIF, and SPP1 signaling pathways, whereas T cells mainly communicate via MHC-I, CCL, and CXCL pathways. Mendelian randomization (MR) identified NT5E and NRG1 as protective factors and S100B as a risk factor. Molecular docking revealed trametinib and AZD8055 as potential therapeutic agents. Our study established a prognostic model based on GRGs, revealed causal relationships between NT5E, NRG1, and S100B with BC prognosis, and elucidated the connection between glycolytic activity and immune microenvironment remodeling. These findings expand our understanding of metabolic reprogramming mechanisms in breast cancer and provide a theoretical foundation for precision therapeutic strategies based on the metabolism-immune axis, potentially improving clinical outcomes for BC patients.