Bioinformatic Analysis of Expression Data from Patients with Multiple Sclerosis

Multiple sclerosis (MS) is an autoimmune neurodegenerative disease whose prevalence has increased. MS is a disease that destroys the myelin sheath of nerve cells in the central nervous system. In the present study, microarray technology and bioinformatics tools were used to identify genes and their interaction pathways to investigate common molecular mechanisms. Additionally, on the basis of the results of this analysis, drug predictions for the treatment of MS were made. Microarray data from the NCBI database, specifically from the GEO section related to GSE41890, containing information on gene expression in 68 samples, were extracted. The two groups, normal and treatment, were subsequently compared. The R programming language was used to analyze the differentially expressed genes (DEGs), and the desired molecular network was constructed. The protein‒protein interaction (PPI) network was created via STRING, and PPI network module analysis was performed via Cytoscape. To investigate protein‒drug interactions, NetworkAnalyst was used. Finally, docking operations were performed via PyRx software. A total of 1190 DEGs, which were involved mainly in cell immunity, the cell cycle, cell proliferation, and signal transduction, were identified. The PPI network contained 67 nodes and 629 interactions. Three protein targets and fifty-one drug candidates were identified; specifically, approximately 11 drugs were linked to KIF11 , 33 drugs were linked to CCNA2 , and 7 drugs were linked to CDK1 . A total of 99443535, 5005498, and 4566 compounds were generally connected to KIF11 , CDK1 , and CCNA2 , respectively.