Integrated in silico and experimental analysis identifies microRNAs from extracellular vesicle-enriched fractions associated with multiple sclerosis

MicroRNAs (miRNAs) are key post-transcriptional regulators implicated in neuroinflammatory and neurodegenerative processes underlying multiple sclerosis (MS). This study aimed to identify candidate miRNAs associated with MS and investigate their involvement in disease-related molecular pathways. An integrated in silico workflow was used to identify MS-associated genes, predict regulatory miRNAs, and characterize enriched pathways.

Candidate miRNAs were validated in serum EV-enriched fractions from treated and untreated MS patients and healthy controls using quantitative polymerase chain reaction (qPCR). In silico analysis prioritized four miRNAs (miR-204-5p, miR-17-5p, miR-21-5p, and miR-106a-5p) for validation. Their predicted target genes were enriched in pathways related to immune regulation and central nervous system function, including Th17 cell differentiation, MAPK signaling, cytokine signaling, and axon guidance. qPCR revealed significant differences in miR-21-5p and miR-106a-5p expression between MS patients and healthy controls, while miR-204-5p differed between untreated MS patients and healthy controls.

Among the four miRNAs evaluated, miR-21-5p was significantly associated with MS status and showed moderate discrimination between MS patients and healthy controls (AUC = 0.7204). These findings identify serum EV-enriched miRNAs as candidate biomarkers of MS, although validation in larger and longitudinal cohorts is required. The authors sincerely thank all patients with multiple sclerosis and healthy volunteers who participated in this study.

This research received no external funding. Laboratory of Molecular Neurobiology, Neuroscience Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania Edita Kuncevičienė, Andrėja Strigauskaitė, Violeta Belickienė & Paulina Vaitkienė Department of Medical Biology and Genetics, Sarajevo Medical School, University Sarajevo School of Science and Technology, Sarajevo, Bosnia and Herzegovina Lejla Kadrić, Aida Ombašić, Aida Hajdarpašić & Anida Malagić Research Institute Verlab for Biomedical Engineering, Medical Devices and Artificial Intelligence, Sarajevo, Bosnia and Herzegovina Department of Neurology, Lithuanian University of Health Sciences, Kaunas, Lithuania The authors declare no competing interests. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Kuncevičienė, E., Kadrić, L., Ombašić, A. et al.