Integrating Single-Cell and Bulk RNA Sequencing to Identify T Cell Proliferation-Associated Genes for Breast Cancer Subtyping and Prognostic Modeling

Background: Significant advancements have been made in immunotherapy for breast cancer (BC), with the proliferation and functional modulation of T cell subsets enhancing anti-tumor immune responses. However, the precise relationship between BC and T cell proliferation remains unclear. This study aimed to identify prognostic genes relevant to BC treatment strategies. Methods: BC-related datasets were obtained from public databases. Prognostic genes were identified through differential expression analysis and multiple regression analysis, which served as the foundation for constructing risk models. Subsequently, nomogram analysis, immune infiltration assessment, drug sensitivity evaluation, and enrichment analyses were conducted. Single-cell data were then utilized to identify key cell populations, analyze their communication roles and differentiation mechanisms, and assess prognostic gene expression trends. Finally, fresh breast cancer tissues were analyzed using qPCR to validate the identified prognostic genes. Results: Eight prognostic genes associated with T cell proliferation in BC were identified: CD24, RAC2, BATF, CCL19, IL18, JAK2, PLA2G2D, and FOXJ1. The risk model demonstrated that BC patients in the high-risk group exhibited lower survival rates, while the nomogram effectively predicted BC survival outcomes. Among the 14 differential immune cell types, 12 displayed higher infiltration levels in the low-risk group. A strong correlation was observed between prognostic genes and specific immune cell populations, such as PLA2G2D and CD8+ T cells (correlation coefficient = 0.51, p