Epigenetic profiling for prognostic stratification and personalized therapy in breast cancer
BackgroundThe 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 mo...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Immunology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1510829/full |
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| author | Xiao Guo Chuanbo Feng Jiaying Xing Yuyan Cao Tengda Liu Wenchuang Yang Runhong Mu Tao Wang |
| author_facet | Xiao Guo Chuanbo Feng Jiaying Xing Yuyan Cao Tengda Liu Wenchuang Yang Runhong Mu Tao Wang |
| author_sort | Xiao Guo |
| collection | DOAJ |
| description | BackgroundThe 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.MethodsWe 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.ResultsMLEM 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.ConclusionMLEM 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. |
| format | Article |
| id | doaj-art-c1c8213d431b45538d453a619eb70dfc |
| institution | Kabale University |
| issn | 1664-3224 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Immunology |
| spelling | doaj-art-c1c8213d431b45538d453a619eb70dfc2025-01-14T06:10:41ZengFrontiers Media S.A.Frontiers in Immunology1664-32242025-01-011510.3389/fimmu.2024.15108291510829Epigenetic profiling for prognostic stratification and personalized therapy in breast cancerXiao Guo0Chuanbo Feng1Jiaying Xing2Yuyan Cao3Tengda Liu4Wenchuang Yang5Runhong Mu6Tao Wang7School of Pharmacy, Beihua University, Jilin, ChinaSchool of Pharmacy, Beihua University, Jilin, ChinaSchool of Pharmacy, Beihua University, Jilin, ChinaSchool of Pharmacy, Beihua University, Jilin, ChinaSchool of Pharmacy, Beihua University, Jilin, ChinaSchool of Pharmacy, Beihua University, Jilin, ChinaSchool of Basic Medical Sciences, Beihua University, Jilin, ChinaResearch Laboratory Center, Guizhou Provincial People’s Hospital, Guiyang, Guizhou, ChinaBackgroundThe 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.MethodsWe 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.ResultsMLEM 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.ConclusionMLEM 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.https://www.frontiersin.org/articles/10.3389/fimmu.2024.1510829/fullbreast cancerepigeneticsmachine learningpersonalized therapyvincristine |
| spellingShingle | Xiao Guo Chuanbo Feng Jiaying Xing Yuyan Cao Tengda Liu Wenchuang Yang Runhong Mu Tao Wang Epigenetic profiling for prognostic stratification and personalized therapy in breast cancer Frontiers in Immunology breast cancer epigenetics machine learning personalized therapy vincristine |
| title | Epigenetic profiling for prognostic stratification and personalized therapy in breast cancer |
| title_full | Epigenetic profiling for prognostic stratification and personalized therapy in breast cancer |
| title_fullStr | Epigenetic profiling for prognostic stratification and personalized therapy in breast cancer |
| title_full_unstemmed | Epigenetic profiling for prognostic stratification and personalized therapy in breast cancer |
| title_short | Epigenetic profiling for prognostic stratification and personalized therapy in breast cancer |
| title_sort | epigenetic profiling for prognostic stratification and personalized therapy in breast cancer |
| topic | breast cancer epigenetics machine learning personalized therapy vincristine |
| url | https://www.frontiersin.org/articles/10.3389/fimmu.2024.1510829/full |
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