The single-cell atlas of programmed cell death signature: A machine learning-based prognostic framework in breast cancer

Breast cancer remains a leading cause of cancer-related mortality in women, and current prognostic models are suboptimal. The transcriptomic role of programmed cell death (PCD) in breast cancer progression is not fully understood. Here, we integrated single-cell RNA sequencing data from breast tumors with nine bulk transcriptomic cohorts to systematically analyze 19 PCD modalities. Using a machine learning framework incorporating 14 algorithms, we constructed a prognostic signature, with a ridge regression-based PCD riskscore showing optimal performance and being further integrated into a clinical nomogram. Functional roles of key genes were validated through in vitro and in vivo experiments. We identified a prognostic signature comprising 26 core PCD genes, which effectively stratified patients into distinct risk groups and robustly predicted overall survival. Single-cell analyses revealed that a high PCD risk core was associated with an immunosuppressive tumor microenvironment and reduced immune checkpoint expression, whereas low-risk patients showed greater sensitivity to targeted therapies. Among the signature genes, PDIA4 was consistently overexpressed in 50 paired breast cancer tissues, and its knockdown markedly inhibited tumor growth and malignant phenotypes. This study establishes a novel PCD-based prognostic signature for breast cancer and identifies PDIA4 as a functionally important oncogene.