{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Hyon JY"],"funding":["Korea Basic Science Institute","Severance Hospital Research Fund for Clinical Excellence grant","National Research Foundation of Korea","Korea Environmental Industry and Technology Institute"],"pagination":["e70089"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12183389"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14(6)"],"pubmed_abstract":["We explored the diagnostic utility of tumor-derived extracellular vesicles (tdEVs) in breast cancer (BC) by performing comprehensive proteomic profiling on plasma samples from 130 BC patients and 40 healthy controls (HC). Leveraging a microfluidic chip-based 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 perfor"],"journal":["Journal of extracellular vesicles"],"pubmed_title":["Extracellular Vesicle Proteome Analysis Improves Diagnosis of Recurrence in Triple-Negative Breast Cancer."],"pmcid":["PMC12183389"],"funding_grant_id":["C512110","RS‐2025‐00518367","C-2023-0006","2021R1A2C3011254","2021R1C1C2007646","RS-2024-00432946","1485019157","2014R1A6A9064166","2020003030007","RS-2024-00463065","C523100","RS‐2024‐00432946","RS-2025-00518367","RS‐2024‐00463065"],"pubmed_authors":["Gawk H","Yang JY","Lee S","Moon S","Han EH","Lee H","Jung HI","Park S","Kim MW","Ha S","Hyun KA","Kim SI","Yang Y","Kim JY","Hyon JY","Kim Y","Chung YH"],"additional_accession":[]},"is_claimable":false,"name":"Extracellular Vesicle Proteome Analysis Improves Diagnosis of Recurrence in Triple-Negative Breast Cancer.","description":"We explored the diagnostic utility of tumor-derived extracellular vesicles (tdEVs) in breast cancer (BC) by performing comprehensive proteomic profiling on plasma samples from 130 BC patients and 40 healthy controls (HC). Leveraging a microfluidic chip-based 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 perfor","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Jun","modification":"2026-06-03T07:04:20.229Z","creation":"2026-04-25T03:22:07.833Z"},"accession":"S-EPMC12183389","cross_references":{"pubmed":["40545963"],"doi":["10.1002/jev2.70089"]}}