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However, the specific impact of long non\u2010coding RNAs (lncRNAs) on EMT in BRCA remains uncertain. In this study, a prognostic model was constructed using EMT\u2010associated lncRNAs (EALs), with utilization of integrative machine learning algorithms. The optimal model consisted of 15 EALs, with an AUC of 0.89 at 5 years, showing its potential as a plausible biomarker for BRCA. Among high\u2010risk individuals, a significant increase in pathways linked to the preservation of equilibrium and immune defense was observed. Moreover, it was indicated that immunotherapy elicited negative responses in this group. Somatic mutations displayed higher\n                    <jats:italic>TP53<\/jats:italic>\n                    rates in high\u2010risk patients and increased\n                    <jats:italic>CDH1\/PIK3CA<\/jats:italic>\n                    in low\u2010risk ones. Notably,\n                    <jats:italic>AC055854.1<\/jats:italic>\n                    and\n                    <jats:italic>MIR205HG<\/jats:italic>\n                    , important EALs in the model, probably regulate BRCA development through the lncRNA\u2010microRNA\u2010mRNA axis. Spatial transcriptome analysis revealed higher expression levels of EALs and high\u2010risk related genes in ductal carcinoma in situ (DCIS), invasive mixed ductal\/lobular carcinoma (IDC), and triple\u2010negative BRCA (TNBC) than in breast metastasis (BMS) samples. And neutrophils were exclusively observed within the tumor microenvironment (TME) of BMS. All these findings emphasized EALs\u2019 value in revolutionizing clinical decision\u2010making for personalized treatment strategies in BRCA cases.\n                  <\/jats:p>","DOI":"10.1002\/biot.70229","type":"journal-article","created":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T08:52:06Z","timestamp":1776675126000},"source":"Crossref","is-referenced-by-count":0,"title":["Multi\u2010Machine Learning Elucidates Clinical Potential of Epithelial\u2010Mesenchymal Transition\u2010Associated Long Non\u2010Coding RNAs in Breast Cancer Progression"],"prefix":"10.1002","volume":"21","author":[{"given":"Rui","family":"Zhu","sequence":"first","affiliation":[{"name":"School of Life Science Nanjing Normal University  Nanjing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Life Science Nanjing Normal University  Nanjing 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