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Updated: Jun 12, 2026
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Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material
Published on: August 1, 2018
Integrative single-cell and spatial transcriptomics with machine learning identify a Luminal-inflam malignant program
Jinpeng Wu1, Jingjing Fan1, Tong Sha1
1Department of Breast and Thyroid Surgery, Affiliated Tumor Hospital of Xinjiang Medical University, Urumqi, China.
Scientific Reports
|June 10, 2026
Summary
Researchers identified a specific malignant epithelial cell state in triple-negative breast cancer (TNBC) and a 12-gene signature for predicting patient survival and potential drug responses.
Area of Science:
- Oncology
- Genomics
- Systems Biology
Background:
- Triple-negative breast cancer (TNBC) exhibits significant cellular heterogeneity.
- Limited targeted therapies exist for TNBC, leading to poor prognoses.
Purpose of the Study:
- To characterize malignant epithelial cell programs in TNBC.
- To identify prognostic biomarkers for TNBC.
- To explore potential therapeutic vulnerabilities.
Main Methods:
- Integrated single-cell and spatial transcriptomic profiling.
- Network-based analyses and machine learning.
- Gene regulatory network analysis and survival analysis.
Main Results:
- Identified a 'Luminal_inflam' malignant epithelial subpopulation with high copy number variation and cell cycle activity.
- Discovered a fibroblast-associated S100A4-EGFR axis potentially regulating this subpopulation.
- Developed a 12-gene risk signature that stratified overall survival and predicted immune features and drug sensitivities.
- Linked RPN1 (Ribophorin 1) depletion to ER homeostasis perturbation, increased oxidative stress, and apoptosis in TNBC cells, with partial rescue by 4-PBA.
Conclusions:
- Delineated a key malignant epithelial state in TNBC.
- RPN1-associated proteostasis vulnerability presents a potential therapeutic target.
- The 12-gene signature offers a framework for risk stratification and personalized treatment strategies.