aceThe 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…
A ridge regression-based 26-gene programmed cell death riskscore built from single-cell and nine bulk cohorts stratified breast cancer patients by risk and predicted overall survival, and PDIA4 knockdown reduced tumor growth.
Evidence
- Peer-reviewedJournal of Biomedical Research2026-07-25
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Truvace Impact Record TRV-2026-0561, v1: “The single-cell atlas of programmed cell death signature: A machine learning-based prognostic framework in breast cancer.” Truvace, 2026-07-25. /record/TRV-2026-0561 (accessed at citation time). sha256 2a6d3c39d223073b…
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