Jiaming He,1,2 Aiman Akhtar,1 Jing Li,2 Qiang Wei,3 Yang Yuan,1 Jianhua Ran,4 Yongping Ma,1 Dilong Chen2,5,6 

1Department of Biochemistry and Molecular Biology, Basic Medical College, Molecular Medicine & Cancer Research Center, Chongqing Medical University, Chongqing, 400016, People’s Republic of China; 2Laboratory of Stem Cells and Tissue Engineering, Department of Histology and Embryology, Chongqing Medical University, Chongqing, 400016, People’s Republic of China; 3Department of Laboratory Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, People’s Republic of China; 4Department of Anatomy, Laboratory of Neuroscience and Tissue Engineering, Basic Medical College, Chongqing Medical University, Chongqing, 400016, People’s Republic of China; 5Chongqing Key Laboratory of Development and Utilization of Genuine Medicinal Materials in Three Gorges Reservoir Area, Chongqing Three Gorges Medical College, Chongqing, 404120, People’s Republic of China; 6NMPA Key Laboratory for Quality Monitoring of Narcotic Drugs and Psychotropic Substances, Chongqing Institute for Food and Drug Control, Chongqing, 401120, People’s Republic of China

Correspondence: Yongping Ma, Email mayongping@cqmu.edu.cn Dilong Chen, Email chendilong@aliyun.com

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.

Keywords: single cell sequencing, breast cancer, scPagwas, Mendelian randomization, bioinformatics