Introduction: MS is an autoimmune disease in which the central nervous system is demyelinated and nerve signal conduction is impaired. In 2023, 2.9 million people were diagnosed with this disease. Despite extensive studies in MS, understanding its pathogenic mechanisms still faces numerous challenges. Today, bioinformatics can provide new biomarkers by analyzing gene expression data to understand the mechanism, prognosis, and diagnosis of various diseases, including MS.
Methods: Gene expression data from peripheral blood samples of MS patients and healthy individuals were obtained from the NCBI GEO database. Differentially expressed genes (DEGs) were identified using Adj p-values≤0.01 and -2 ≥ Log FC ≥ 2. A protein-protein interaction (PPI) network was constructed from the DEGs using the STRING database and plotted and analyzed with Cytoscape. Functional enrichment analysis was performed using GeneCodis. Target miRNAs for the PPI network genes were predicted using the miRWalk database, and a miRNA-mRNA regulatory network was constructed and analyzed in Cytoscape.
Results: Using the obtained DEGs and the STRING database, a PPI network was constructed and analyzed by Cytoscape, identifying key genes associated with MS. Further analysis using miRWalk identified seven miRNAs that target genes within this network. These miRNAs showed strong associations with MS and are potential candidates for use as biomarkers.
Conclusion: This bioinformatics-based study identified hsa-miR-422a, hsa-miR4650-3p, hsa-miR-4754, hsa-miR-665, hsa-miR-6833-5p, hsa-miR-6875-5-R, and has-mir-7110-5p as potential biomarkers for MS. These findings warrant further in vitro investigations to confirm their diagnostic and prognostic utility and assess their potential for clinical application. Future studies should focus on experimental validation of these miRNAs in larger patient cohorts and investigate their potential as therapeutic targets.