Childhood epilepsy poses a serious threat to patients’ growth, development, and life safety, creating an urgent need for precise, non-invasive, and longitudinally monitorable biomarkers. Previous studies have confirmed that extracellular vesicles (EVs) with abnormal N-glycosylation modifications regulate various neurological disorders, where characteristic N-glycans serve as potential diagnostic markers. In this study, we systematically compared the properties of EVs isolated via three different methods. The results demonstrated that an exosome purification filter column (EPF) combined with ultrafiltration emerged as the optimal approach for isolating EVs from large-scale clinical samples. Subsequent matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS)-based glycomic profiling of EVs and serum revealed distinct N-glycan signatures. Utilizing a novel two-step machine learning model, we identified 47 characteristic N-glycans in EVs as biomarkers for epilepsy diagnosis and classification. These biomarkers effectively distinguished between normal, focal, and generalized epilepsy subtypes while also exhibiting superior diagnostic performance compared to serum N-glycan profiles. Furthermore, we constructed a correlation network map of glycans, which highlighted dynamic alterations in the expression patterns of EV glycans during epileptogenesis. Taken together, the N-glycans of EVs exhibit promising potential as biomarkers for epilepsy detection, offering new insights into non-invasive diagnosis and disease monitoring.
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