Advanced machine learning unveils CD8 + T cell genetic markers enhancing prognosis and immunotherapy efficacy in breast cancer
Haodi Ma,
LinLin Shi,
Jiayu Zheng,
Li Zeng,
Youyou Chen,
Shunshun Zhang,
Siya Tang,
Zhifeng Qu,
Xin Xiong,
Xuewei Zheng,
Qinan Yin
Affiliations
Haodi Ma
Precision Medicine Laboratory, School of Medical Technology and Engineering, Henan University of Science and Technology
LinLin Shi
State Key Laboratory of Esophageal Cancer Prevention & Treatment, Henan Key Laboratory of Microbiome and Esophageal Cancer Prevention and Treatment, Henan Key Laboratory of Cancer Epigenetics, Cancer Hospital, The First Affiliated Hospital, College of Clinical Medicine, Medical College of Henan University of Science and Technology
Jiayu Zheng
Precision Medicine Laboratory, School of Medical Technology and Engineering, Henan University of Science and Technology
Li Zeng
Precision Medicine Laboratory, School of Medical Technology and Engineering, Henan University of Science and Technology
Youyou Chen
Precision Medicine Laboratory, School of Medical Technology and Engineering, Henan University of Science and Technology
Shunshun Zhang
Precision Medicine Laboratory, School of Medical Technology and Engineering, Henan University of Science and Technology
Siya Tang
Precision Medicine Laboratory, School of Medical Technology and Engineering, Henan University of Science and Technology
Zhifeng Qu
Radiology Department, The First Affiliated Hospital of Henan University of Science and Technology
Xin Xiong
Department of Pathology, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University
Xuewei Zheng
Precision Medicine Laboratory, School of Medical Technology and Engineering, Henan University of Science and Technology
Qinan Yin
Precision Medicine Laboratory, School of Medical Technology and Engineering, Henan University of Science and Technology
Abstract Background Breast cancer (BC) is the most common cancer in women and poses a significant health burden, especially in China. Despite advances in diagnosis and treatment, patient variability and limited early detection contribute to poor outcomes. This study examines the role of CD8 + T cells in the tumor microenvironment to identify new biomarkers that improve prognosis and guide treatment strategies. Methods CD8 + T-cell marker genes were identified using single-cell RNA sequencing (scRNA-seq), and a CD8 + T cell-related gene prognostic signature (CTRGPS) was developed using 10 machine-learning algorithms. The model was validated across seven independent public datasets from the GEO database. Clinical features and previously published signatures were also analyzed for comparison. The clinical applications of CTRGPS in biological function, immune microenvironment, and drug selection were explored, and the role of hub genes in BC progression was further investigated. Results We identified 71 CD8 + T cell-related genes and developed the CTRGPS, which demonstrated significant prognostic value, with higher risk scores linked to poorer overall survival (OS). The model’s accuracy and robustness were confirmed through Kaplan-Meier and ROC curve analyses across multiple datasets. CTRGPS outperformed existing prognostic signatures and served as an independent prognostic factor. The role of the hub gene TTK in promoting malignant proliferation and migration of BC cells was validated. Conclusion The CTRGPS enhances early diagnosis and treatment precision in BC, improving clinical outcomes. TTK, a key gene in the signature, shows promise as a therapeutic target, supporting the CTRGPS’s potential clinical utility.
