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Leveraging a microfluidic chip\u2010based isolation technique optimized for low plasma volume and effective contaminant depletion, we achieved efficient enrichment of tdEVs. Proteomic analysis identified 26 candidate biomarkers differentially expressed between BC patients and HCs. To enhance biomarker selection robustness, we implemented a hybrid machine learning framework integrating LsBoost, convolutional neural networks, and support vector machines. Among the identified candidates, four EV proteins. ECM1, MBL2, BTD, and RAB5C. not only exhibited strong discriminatory performance, particularly for triple\u2010negative breast cancer (TNBC), but also demonstrated potential relevance to disease recurrence, providing prognostic insights beyond initial diagnosis. Receiver operating characteristic (ROC) curve analysis demonstrated high diagnostic accuracy with an area under the curve (AUC) of 0.924 for BC and 0.973 for TNBC, as determined by mass spectrometry. These findings were further substantiated by immuno assay validation, which yielded an AUC of 0.986 for TNBC. Collectively, our results highlight the potential of EV proteomics as a minimally invasive, blood\u2010based platform for both accurate detection and recurrence risk stratification in breast cancer and its aggressive subtypes, offering promising implications for future clinical applications.<\/jats:p>","DOI":"10.1002\/jev2.70089","type":"journal-article","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T03:49:53Z","timestamp":1750650593000},"source":"Crossref","is-referenced-by-count":9,"title":["Extracellular Vesicle Proteome Analysis Improves Diagnosis of Recurrence in Triple\u2010Negative Breast Cancer"],"prefix":"10.1002","volume":"14","author":[{"given":"Ju\u2010Yong","family":"Hyon","sequence":"first","affiliation":[{"name":"Research Center for Digital Omics Korea Basic Science Institute Cheongju Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min Woo","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Surgery Yonsei University College of Medicine Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyung\u2010A","family":"Hyun","sequence":"additional","affiliation":[{"name":"School of Biopharmaceutical and Medical Sciences Sungshin Women's University Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yeji","family":"Yang","sequence":"additional","affiliation":[{"name":"Research Center for Digital Omics Korea Basic Science Institute Cheongju Republic of Korea"},{"name":"Critical Diseases Diagnostics Convergence Research Center Korea Research Institute of Bioscience and Biotechnology Daejeon Republic of Korea"},{"name":"Department of Biological Sciences Korea Advanced Institute for Science and Technology (KAIST) Daejeon Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seongmin","family":"Ha","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering Yonsei University Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jee Ye","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Surgery Yonsei University College of Medicine Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Surgery Yonsei University College of Medicine Seoul Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sunyoung","family":"Park","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering Yonsei University Seoul Republic of Korea"},{"name":"The DABOM Inc. 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