Non-apoptotic regulatory cell death scoring system to predict the clinical outcome and drug choices in breast cancer
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View abstract on PubMed
Summary
This summary is machine-generated.This study identifies a novel risk signature based on non-apoptotic regulatory cell death genes (NRGs) for breast cancer (BC) prognosis. This signature helps predict patient outcomes and guides clinical decisions.
Area Of Science
- Oncology
- Genomics
- Molecular Biology
Background
- Breast cancer (BC) is a leading cause of cancer death in women worldwide.
- Non-apoptotic regulatory cell death (RCD) plays a significant role in BC pathogenesis and progression.
Purpose Of The Study
- To identify molecular subtypes of breast cancer based on non-apoptotic RCD.
- To develop a prognostic model using non-apoptotic RCD genes (NRGs) for breast cancer patients.
Main Methods
- Utilized TCGA and GEO databases for RNA sequencing and clinical data.
- Employed non-negative matrix factorization (NMF) for molecular subtyping.
- Applied machine learning algorithms (lasso, random forest, XGBoost) to identify prognostic NRGs and construct a risk signature.
- Validated gene expression using RT-qPCR, scRNA-seq, and HPA database.
Main Results
- Identified three distinct non-apoptotic RCD-related molecular subtypes in breast cancer.
- Discovered a risk signature comprising 5 differentially expressed NRGs.
- Demonstrated significant differences in clinicopathological features, drug sensitivity, and prognosis among risk groups.
- Developed a nomogram integrating the risk signature, age, and TNM staging for survival prediction.
Conclusions
- A novel NRG-related risk signature was identified as a potential prognostic marker for breast cancer.
- The findings may aid in personalized treatment strategies and improved patient outcomes.
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