糖基转移酶相关亚型的综合分析揭示了乳腺癌中的预后基因和免疫景观

Breast cancer (BC) exhibits extensive heterogeneity, complicating personalized treatment. Glycosyl transferases (GTs) are potential biomarkers for cancer treatment. Consensus clustering was performed on GT-related genes (GTRGs) in the The Cancer Genome Atlas Breast Cancer cohort. Clinical characteristics, survival outcomes, and molecular pathways were analyzed. Differences in the tumor microenvironment were assessed.

Machine learning techniques (Lasso, random forest, XGBoost) identified hub GTRGs. A prognostic nomogram was constructed integrating hub GTRGs and clinical features. The cell validation experiments were finally performed. Two subtypes (C1 and C2) were identified, and C1 showed better survival than C2 (P = . 0029). C1 had higher CD8 T cells and activated NK cells, while macrophage fractions were lower.

In addition, GTs-related mTORC1 and PI3K/AKT/mTOR pathways were suppressed, whereas p53, Wnt/β-Catenin, and Notch pathways were activated. 4 hub GTRGs (ATP5F1B, CS, EPB41L4B, and LIMCH1) were identified. High expression of these genes correlated with poorer prognosis. The nomogram demonstrated high accuracy of this hub GTRGs for predicting 1-, 3-, and 5-year overall survival.

Additionally, expression levels of 4 hub genes were higher in the BC cell line MDA-MB-415, and CS knockdown significantly inhibited the proliferation and migration of MDA-MB-415 cells. GT-related subtypes in BC are linked to distinct survival outcomes and immune landscapes. Hub GTRGs are valuable prognostic markers, aiding personalized treatment strategies.