Glycosylated protein-related microenvironmental features in breast cancer are associated with patient prognosis.

The tumor microenvironment (TME) and aberrant glycosylation have been suggested to play key roles in cancer. This study integrated differentially expressed genes (DEGs) and weighted gene coexpression network analysis (WGCNA) to identify tumor microenvironment-related genes and construct a TME-risk prognostic signature (TMERS) through LASSO Cox regression. After batch effect removal, 44 TME-prognosis-related genes (TMEPGs) were identified and classified into three molecular subtypes via K-means clustering. The finalized 22-gene TMERS model demonstrated robust prognostic predictive capacity in GEO datasets. The results revealed distinct immune profiles and prognostic stratifications among genetic subtypes and risk groups, confirming that the TMERS is an independent prognostic indicator for breast cancer (BRCA). Glycosyltransferase genes (GTs) have potential therapeutic relevance through immune regulation, with TMEPG member killer cell lectin like receptor B1 (KLRB1) significantly correlated with BRCA prognosis. Cellular experiments demonstrated that KLRB1 overexpression suppressed BRCA cell proliferation and migration. This work establishes a novel prognostic model for BRCA while highlighting KLRB1 as a potential biomarker, providing new insights into TME-targeted therapeutic strategies.