BACKGROUND:  The tumor microenvironment and biomarkers play a pivotal role in breast cancer research, yet there remains a pressing need for effective biomarkers. This study focuses on identifying a novel IGKC+ T Cell subpopulation and its related biomarkers to pave the way for innovative targeted therapies and improved clinical outcomes. METHODS: We first performed single-cell RNA sequencing (scRNA-seq) analysis to characterize immune cell heterogeneity within the tumor microenvironment, leading to the identification of series cell subpopulation. Then, by performing univariate analysis to correlate cell proportions with patient prognosis, we identified a novel IGKC+ T cell subpopulation. Next, we applied bulk RNA-seq deconvolution algorithms to estimate the abundance of this subpopulation across breast cancer cohorts. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to identify genes associated with the IGKC+ T cell population. To pinpoint key regulatory genes, we applied machine learning algorithms. Based on the hub genes identified, we constructed a prognostic risk model and developed a nomogram to aid clinical decision-making. Immune infiltration patterns were further assessed in high- vs low-risk groups defined by the model. Finally, functional validation was performed through overexpression of BCL2L14 in vitro, and downstream signaling pathways were examined. RESULTS: We identified the novel IGKC+ T cell subpopulation and core genes. Machine learning pinpointed BCL2L14, IGHD, MAPT-AS1, NT5DC4, and TNIP3 as key regulators of breast cancer progression in this subpopulation. The model stratified patients into high- and low-risk groups, with high-risk patients showing worse prognosis and weaker immune infiltration. Overexpression of BCL2L14 was experimentally demonstrated to accelerate breast cancer progression, linked to enhanced phosphorylation of the NF-κB pathway. CONCLUSION: Our results underscore BCL2L14 as a potential driver within the novel T-cell subpopulation and a critical biomarker for breast cancer diagnosis. These findings provide a basis for developing advanced diagnostic tools and targeted therapies, which may ultimately enhance patient prognosis.