ArticleFrontiers in immunology2024
Epigenetic profiling for prognostic stratification and personalized therapy in breast cancer.
Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Histone modifications across cancers: mechanisms, therapy and clinical translation.Molecular cancer · 2026Review
- The Future of Epigenetics: Emerging Technologies and Clinical Applications.ACS pharmacology & translational science · 2026Review
- Clinical Relevance of USP44 Expression and DNA Methylation Status in Breast Cancer Cell Lines, Tumor Tissues, and Circulating Tumor DNA.Breast cancer (Dove Medical Press) · 2026Article
- Epigenetic Regulation of Autophagy in Breast Cancer: Implications for Biomarker Discovery and Personalized Therapy.Cancer reports (Hoboken, N.J.) · 2025Review
- Informatics at the Frontier of Cancer Research.Cancer research · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The rising incidence of breast cancer and its heterogeneity necessitate precise tools for predicting patient prognosis and tailoring personalized treatments. Epigenetic changes play a critical role in breast cancer progression and therapy responses, providing a foundation for prognostic model development. Methods: We developed the Machine Learning-derived Epigenetic Model (MLEM) to identify prognostic epigenetic gene patterns in breast cancer. Using multi-cohort transcriptomic datasets, MLEM was constructed with rigorous machine learning techniques and validated across independent datasets. The model's performance was further corroborated through immunohistochemical validation on clinical samples. Results: MLEM effectively stratified breast cancer patients into high- and low-risk groups. Low-MLEM patients exhibited improved prognosis, characterized by enhanced immune cell infiltration and higher responsiveness to immunotherapy. High-MLEM patients showed poorer prognosis but were more responsive to chemotherapy, with vincristine identified as a promising therapeutic option. The model demonstrated robust performance across independent validation datasets. Conclusion: MLEM is a powerful prognostic tool for predicting breast cancer outcomes and tailoring personalized treatments. By integrating epigenetic insights with machine learning, this model has the potential to improve clinical decision-making and optimize therapeutic strategies for breast cancer patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.