Deciphering crucial genes in multiple sclerosis pathogenesis and drug repurposing: A systems biology approach

This study employed systems biology and high-throughput technologies to analyze complex molecular compo-nents of MS pathophysiology, combining data from multiple omics sources to identify potential biomarkers and propose therapeutic targets and repurposed drugs for MS treatment. This study analyzed GEO microarray datasets and MS proteomics data using geWorkbench, CTD, and COR-EMINE to identify differentially expressed genes associated with MS disease. Protein-protein interaction net-works were constructed using Cytoscape and its plugins, and functional enrichment analysis was performed to identify crucial molecules. A drug-gene interaction network was also created using DGIdb to propose medications. This study identified 592 differentially expressed genes (DEGs) associated with MS disease using GEO, pro-teomics, and text-mining datasets. 37 DEGs were found to be important by topographical network studies, and 6 were identified as the most significant for MS pathophysiology. Additionally, we proposed six drugs that target these key genes. Crucial molecules identified in this study were dysregulated in MS and likely play a key role in the disease mechanism, warranting further research. Additionally, we proposed repurposing certain FDA-approved drugs for MS treatment. Our in silico results were supported by previous experimental research on some of the target genes and drugs. Significance: As the long-lasting investigations continue to discover new pathological territories in neuro-degeneration, here we apply a systems biology approach to determine multiple sclerosis's molecular and path-ophysiological origin and identify multiple sclerosis crucial genes that contribute to candidating new biomarkers and proposing new medications.

Automated summary

This study utilized systems biology and high-throughput technologies to analyze the molecular components of multiple sclerosis (MS) pathophysiology. By combining data from multiple omics sources, potential biomarkers and therapeutic targets were identified, along with repurposed drugs for MS treatment. The study identified differentially expressed genes associated with MS using GEO microarray datasets and MS proteomics data, and constructed protein-protein interaction networks to investigate crucial molecules. Furthermore, a drug-gene interaction network was created to propose medications. The findings provide insights into the pathophysiology of MS and suggest potential targets for treatment.