Predicting breast cancer prognosis using γδ T cell gene signatures

Gamma delta (γδ) T cells exert a pivotal anti-tumor role in the breast cancer (BC) tumor microenvironment, highlighting the importance of investigating their prognostic value for improved patient stratification. We analyzed single-cell ribonucleic acid sequencing data to cluster immune cells and identify marker genes. Prognostic features were selected using Least Absolute Shrinkage and Selection Operator regression in the Cancer Genome Atlas–Breast Invasive Carcinoma cohort and validated across five external Gene Expression Omnibus cohorts. The expression of these prognostic genes was further validated by immunohistochemistry (IHC) in an in-house cohort of BC patients. These selected features were used to construct machine learning models, with the best-performing model undergoing hyperparameter tuning to optimize its performance. γδ T cells were identified as one of the major immune cell populations in BC. Twelve γδ T cell-associated genes were selected based on their prognostic significance in external validation cohorts. The random forest (RF) model achieved the highest accuracy (0.835) after hyperparameter tuning. In external validation, the final model showed the highest performance with area under the curve/accuracy values of 0.81/0.849. IHC analysis confirmed dysregulated expression of key signature proteins in BC tissues. In conclusion, we developed an efficient prognostic RF model based on a 12-gene signature from γδ T cells, which may serve as a clinically valuable tool for risk stratification in BC patients.