A Breast Cancer Prognostic Model Based on Folic Acid Metabolism-Related Genes to Reveal the Immune Landscape

Breast cancer (BC) threatens women's health, and the prognosis is dismal. Folic acid metabolism affects cancer prognosis, but research on folic acid metabolism-related genes (FMRs) in BC is scarce. We used TCGA-BRCA as the training set and GSE21653 as the validation set. Five FMRs (PLAT, SERPINA3, IFNG, SLC19A1, NFKB2) were screened via univariate and LASSO Cox regression analyses, and a prognostic model was built based on multivariate Cox regression analysis. The model showed excellent predictive performance. Differentially expressed genes in high- and low-risk groups were enriched in steroid hormone biosynthesis and neuroactive ligand-receptor interaction pathways. The low-risk group exhibited higher immune cell infiltration and better immunotherapy response. AM-5992 and 5-fluorodeoxyuridine 10mer may be potential BC drugs. This FMR-based model can accurately predict BC prognosis, offering a clinical reference.