{"title":"Innovative Approaches to EMT-Related Biomarker Identification in Breast Cancer: Multi-Omics and Machine Learning Methods.","authors":"Ghazaleh Khalili-Tanha, Alireza Shoari","doi":"10.3390/biotech14030075","DOIUrl":null,"url":null,"abstract":"<p><p>Breast cancer is the most prevalent cancer among women and is challenging to diagnose and treat due to its diverse subtypes and stages. Precision medicine aims to improve early detection, prognosis, and treatment planning by identifying new clinical biomarkers. The review emphasizes the importance of using cutting-edge technology and artificial intelligence (AI) to identify new biomarkers associated with epithelial-mesenchymal transition (EMT). During EMT, epithelial cells transform into a mesenchymal state, a process driven by genetic and epigenetic alterations that facilitate cancer progression. The review discusses how statistical analysis and machine learning methods applied to multi-omics data facilitate the discovery of novel EMT-related biomarkers, thereby advancing therapeutic strategies. This conclusion is supported by numerous clinical and preclinical studies on breast cancer.</p>","PeriodicalId":34490,"journal":{"name":"BioTech","volume":"14 3","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12467340/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"BioTech","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/biotech14030075","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOTECHNOLOGY & APPLIED MICROBIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Breast cancer is the most prevalent cancer among women and is challenging to diagnose and treat due to its diverse subtypes and stages. Precision medicine aims to improve early detection, prognosis, and treatment planning by identifying new clinical biomarkers. The review emphasizes the importance of using cutting-edge technology and artificial intelligence (AI) to identify new biomarkers associated with epithelial-mesenchymal transition (EMT). During EMT, epithelial cells transform into a mesenchymal state, a process driven by genetic and epigenetic alterations that facilitate cancer progression. The review discusses how statistical analysis and machine learning methods applied to multi-omics data facilitate the discovery of novel EMT-related biomarkers, thereby advancing therapeutic strategies. This conclusion is supported by numerous clinical and preclinical studies on breast cancer.
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