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p27 Cell Cycle Inhibitor and Survival in Luminal-Type Breast Cancer: Gene Ontology, Machine Learning, and Drug
In Ah Park1, Yung-Kyun Noh2,3, Kyueng-Whan Min4
1Department of Pathology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Korea.
Journal of Breast Cancer
|September 30, 2024
Summary
Low p27 expression in luminal breast cancer is linked to poorer survival. Machine learning identified p27 as a key survival factor, suggesting new drug targets for personalized treatment strategies.
Area of Science:
- Oncology
- Molecular Biology
- Bioinformatics
Background:
- p27 is a cell cycle inhibitor with established prognostic value in carcinomas.
- Its specific role and therapeutic potential in luminal breast cancer are not well understood.
Purpose of the Study:
- To investigate the functional significance of p27 in luminal breast cancer.
- To identify potential drug targets and therapeutic strategies based on p27 expression.
Main Methods:
- Analysis of clinicopathological data from 868 luminal breast cancer patients.
- Integration of METABRIC and Gene Expression Omnibus datasets (2,355 patients).
- Immunohistochemistry, gene expression analysis, disease ontology, machine learning for survival prediction, and in vitro drug screening.
Main Results:
- Low p27 expression correlated with advanced stage, ER/PR negativity, and reduced survival.
- Reduced CDKN1B (p27 gene) expression linked to proliferation and PRC2 pathways.
- p27 was the second most significant survival predictor, improving ML model performance.
- Cell lines with low CDKN1B showed sensitivity to voxtalisib and serdemetan.
Conclusions:
- Machine learning and bioinformatics analysis of p27 can improve risk stratification.
- p27-targeted strategies may offer personalized treatment options for luminal breast cancer.
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