[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121138-en":3,"doc-seo-121138-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121138,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning-informed liquid-liquid phase separation for personalized breast cancer treatment assessment - research article","Machine learning-derived liquid-liquid phase separation (MDLS) is developed to improve prognostic accuracy and support personalized breast cancer treatment. Using 10 machine learning algorithms, 108 algorithm combinations are built and tested across 14 breast cancer cohorts with 9,723 patients. Genetic mutations, copy number alterations, and single-cell RNA-seq data characterize MDLS mechanisms and predictions, while immune infiltration and checkpoint expression guide immunotherapy targeting and correlation/drug responsiveness inform chemotherapy targets. The MDLS model shows superior prognostic performance and links high-MDLS patients to distinct genomic and cellular features with treatment-related implications.","TYPE Original Research PUBLISHED 19 November 2024 DOI 10.3389/fimmu.2024.1485123  \nOPEN ACCESS  \nEDITED BY  \nHai Fang,  \nShanghai Jiao Tong University, China  \nREVIEWED BY  \nHeba Taher,  \nCairo University, Egypt Yun Chen,  \nXiangtan University, China Wenting Long,  \nYale University, United States  \n*CORRESPONDENCE  \nHuan Chen  \n [Chenjihuan158@163.com](Chenjihuan158@163.com)[ ](Chenjihuan158@163.com)Jing Hou  \n [hjhlingtong@163.com](hjhlingtong@163.com)  \nRECEIVED 23 August 2024  \nACCEPTED 31 October 2024  \nPUBLISHED 19 November 2024  \nCITATION  \nWang T, Wang S, Li Z, Xie J, Chen H and Hou J (2024) Machine learning-informed  \nliquid-liquid phase separation for personalized breast cancer treatment assessment.  \nFront. Immunol. 15:1485123 .  \ndoi: 10.3389/fimmu.2024.1485123  \nCOPYRIGHT  \n© 2024 Wang, Wang, Li, Xie, Chen and Hou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning-informed liquid-liquid phase separation for personalized breast cancer treatment assessment  \nTao Wang 1, Shu Wang 2, Zhuolin Li 2, Jie Xie 2, Huan Chen 2* and Jing Hou 2*  \n1 Research Laboratory Center, Guizhou Provincial People’s Hospital, Guiyang, China, 2 Department of Breast Surgery, Guizhou Provincial People’s Hospital, Guiyang, China  \nBackground: Breast cancer, characterized by its heterogeneity, is a leading cause of mortality among women. The study aims to develop a Machine LearningDerived Liquid-Liquid Phase Separation (MDLS) model to enhance the prognostic accuracy and personalized treatment strategies for breast cancer patients.  \nMethods: The study employed ten machine learning algorithms to construct 108 algorithm combinations for the MDLS model. The robustness of the model was evaluated using multi-omics and single-cell data across 14 breast cancer cohorts, involving 9,723 patients. Genetic mutation, copy number alterations, and single-cell RNA sequencing were analyzed to understand the molecular mechanisms and predictive capabilities of the MDLS model. Immunotherapy targets were predicted by evaluating immune cell infiltration and immune checkpoint expression. Chemotherapy targets were identified through correlation analysis and drug responsiveness prediction.  \nResults: The MDLS model demonstrated superior prognostic power, with a mean C-index of 0 . 649, outperforming 69 published signatures across ten cohorts. High-MDLS patients exhibited higher tumor mutation burden and distinct genomic alterations, including significant gene amplifications and deletions. Single-cell analysis revealed higher MDLS activity in tumor-aneuploid cells and identified key regulatory factors involved in MDLS progression. Cell-cell communication analysis indicated stronger interactions in high-MDLS groups, and immunotherapy response evaluation showed better outcomes for lowMDLS patients.  \nConclusion: The MDLS model offers a robust and precise tool for predicting breast cancer prognosis and tailoring personalized treatment strategies. Its integration of multi-omics and machine learning highlights its potential clinical applications, particularly in improving the effectiveness of immunotherapy and identifying therapeutic targets for high-MDLS patients.  \nKEYWORDS  \nbreast cancer, liquid-liquid phase separation, machine learning, immunotherapy, methotrexate  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \nIntroduction  \nBreast cancer (BC) is a heterogeneous disease and is the most common cancer in women. Breast cancer morbidity and mortality are separately reported as 11.7% and 6.9%, respectively, by GLOBCAN, 2020 (1) . In women, it remain","cbCaioAlwDc0gBx3","https://ap.wps.com/l/cbCaioAlwDc0gBx3","pdf",13265230,1,17,"English","en",105,"# Introduction\n## Background and clinical need for personalized prediction\n## Liquid-liquid phase separation (LLPS) and relevance to cancer biology\n## Gap in using LLPS-related genes for prognosis\n# Methods\n## Model construction using multiple machine learning algorithms\n## Multi-omics and single-cell analysis across cohorts\n## Target prediction for immunotherapy and chemotherapy","[{\"question\":\"What is the MDLS model proposed in the study?\",\"answer\":\"The study develops a machine learning-derived liquid-liquid phase separation (MDLS) model to improve prognostic accuracy and personalize breast cancer treatment strategies.\"},{\"question\":\"How is the MDLS model constructed and validated?\",\"answer\":\"Ten machine learning algorithms generate 108 algorithm combinations, and robustness is evaluated using multi-omics and single-cell data across 14 breast cancer cohorts with 9,723 patients.\"},{\"question\":\"How does the study predict immunotherapy and chemotherapy targets?\",\"answer\":\"Immunotherapy targets are predicted by assessing immune cell infiltration and immune checkpoint expression, while chemotherapy targets are identified using correlation analysis and drug responsiveness prediction.\"}]","Machine learning-informed liquid-liquid phase separation for personalized breast cancer treatment assessment - 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