Multiple sclerosis (MS) is a chronic inflammatory disease characterized by central nervous system (CNS) damage, which can lead to severe physical or cognitive disability, as well as neurological deficits. Currently, there is significant focus on research into MS – searching for new markers, attempting to elucidate the initial pathogenetic pathways of the disease, and predicting disease progression. Biomarkers of demyelinating diseases are particularly important, as sometimes the slow progression of these diseases and nonspecific symptoms can pose significant diagnostic challenges, especially at the onset of the disease, as well as predicting the progression of the disease, the transition from clinically or radiologically isolated syndrome to MS.
The aim of this project is to investigate the associations between changes in patients’ miRNA expression profiles and clinical as well as radiological data using artificial intelligence and data analysis methods. The goal is to distinguish and evaluate the role of these molecules in diagnosing and predicting the progression of multiple sclerosis (MS) and their potential application in personalized treatment.
During the project, researchers from LSMU will collect clinical and radiological (magnetic resonance imaging (MRI)) data from MS patients. For miRNA studies, blood serum of the subjects will be collected, RNA will be extracted, its quality will be evaluated, and samples will be sent for sequencing studies. In this pilot study, it is planned to analyze samples from up to 10 MS patients who have not received immunomodulatory treatment and up to 10 samples from a control group. To find diagnostic and prognostic biomarkers, researchers from KTU will analyze the sequencing results, clinical, and radiological data using various bioinformatics and data analysis programs. The results of this study could be applied to more accurate diagnosis of multiple sclerosis, help assess potential disease progression, and contribute to a personalized treatment strategy.
Project funding:
KTU Research Fund
Project results:
–
Period of project implementation: 2025-04-17 - 2025-12-31
Project partners: Lithuanian University of Health Sciences, Vytautas Magnus University
