Canonical text (the exact bytes fingerprinted)

TRUVACE RECORD VERSION
record: TRV-2026-0561
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-25T06:08:49.491368Z
status: published
lens: g_space
sector: health
headline: The single-cell atlas of programmed cell death signature: A machine learning-based prognostic framework in breast cancer
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: effectively stratified patients into distinct risk groups and robustly predicted overall survival | its knockdown markedly inhibited tumor growth and malignant phenotypes | ridge regression-based PCD riskscore showing optimal performance
problem_reading: (none)
problem_evidence: (none)
quick_read: By the publication date of July 25, 2026, researchers had integrated single-cell RNA sequencing of breast tumors with nine bulk cohorts to study 19 programmed cell death modalities, applying 14 machine learning algorithms to build a 26-gene prognostic signature. The ridge regression-based PCD riskscore was integrated into a clinical nomogram and validated experimentally, including PDIA4 overexpression in 50 paired tissues and functional inhibition studies.

The work matters because it links an AI-derived cell-death signature to measurable patient stratification, tumor microenvironment features, and therapy sensitivity, and nominates PDIA4 as a functionally important oncogene. What remains uncertain is prospective clinical validation, generalizability beyond the studied cohorts, and whether PDIA4 inhibition translates into safe and effective treatment in patients.
limitation: 
tag: Evidence-backed gain
key_points: Integrated single-cell RNA sequencing from breast tumors with nine bulk transcriptomic cohorts to analyze 19 programmed cell death modalities. | Tested 14 machine learning algorithms; ridge regression-based PCD riskscore selected as optimal and integrated into a clinical nomogram with 26 core genes. | High-risk group linked to immunosuppressive tumor microenvironment and reduced immune checkpoint expression; low-risk group showed greater sensitivity to targeted therapies. | PDIA4 was overexpressed in 50 paired breast cancer tissues and functionally validated in vitro and in vivo as an oncogene whose knockdown inhibited malignant phenotypes.
rundown: Researchers combined single-cell RNA sequencing data from breast tumors with nine bulk transcriptomic cohorts to systematically evaluate 19 programmed cell death modalities. A framework testing 14 algorithms produced a 26-gene signature, with a ridge regression-based PCD riskscore showing optimal performance and incorporated into a nomogram.

Single-cell analysis associated high PCD risk with an immunosuppressive tumor microenvironment and reduced immune checkpoint expression, while low-risk patients were more sensitive to targeted therapies. PDIA4 was consistently overexpressed in 50 paired breast cancer tissues, and experimental knockdown inhibited tumor growth and malignant phenotypes in vitro and in vivo.
sources:
- peer_reviewed | Journal of Biomedical Research | https://doi.org/10.7555/jbr.40.20260038 | 2026-07-25
prev: 0000000000000000000000000000000000000000000000000000000000000000

sha256

2a6d3c39d223073bab4b83a38c5700386315992005f96f9af6d87f808ee3ec56

previous

0000000000000000000000000000000000000000000000000000000000000000