인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록·키워드
Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system with heterogenous clinical course, lacking non-invasive biomarkers for phenotype differentiation. This study aimed to explore circulating extracellular vesicle (EV)-derived microRNA (miRNA) signatures and related molecular profiles capable of distinguishing stable relapsing-remitting MS (RRMS) from secondary progressive MS (SPMS). Plasma samples were collected from stable RRMS (n = 30), SPMS (n = 30), and healthy controls (HC) (n = 30), followed by total EVs isolation and characterization using transmission electron microscopy, dynamic light scattering, and flow cytometry. RNA was extracted from EVs, and miRNA profiles were analyzed via RNA sequencing and RT-qPCR. Cytokines and neuronal/astroglial injury biomarkers were quantified using the BioPlex system and ELISA. Functional enrichment and network analyses of miRNA targets were performed, alongside logistic regression modeling to explore potential distinguishing features. Four EV-derived miRNAs (miR-760, miR-98-5p, miR-301a-3p, miR-223-3p) showed significant differences (p < 0.05) between stable RRMS and SPMS. An integrative model combining miRNAs with fibroblast growth factor (FGF) basic protein enabled accurate phenotypic differentiation (AUC = 0.942). miR-760 showed the strongest distinctive capacity for stable RRMS. Additionally, miR-98-5p was markedly up-regulated in both stable RRMS and SPMS compared to HC. Network analysis of miRNA targets suggested distinct immunoregulatory patterns across MS phenotypes. Plasma EV-derived miRNAs—particularly miR-760, and miR-98-5p—showed potential as molecular indicators associated with disease phenotype in MS. Integrating EV-miRNA profiling with protein markers support efforts toward more precise stratification of MS patients. Further studies in independent cohorts and functional validation are warranted before clinical translation.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.