The molecular pathway associated with Multiple sclerosis (MS) is complex and symptomatic treatments are only available right now. Early diagnosis of MS creates a window for healthcare providers to manage the disease more efficiently. Blood-based biomarker study has been done in the past to identify the upregulated and downregulated genes but in this present study, a novel approach has been taken for identifying genes associated with the disease. In this present study, hub genes are identified and the top ten hub genes were used to identify drugs associated with them. Upregulated genes were identified using the dataset GSE21942 (which contains information related to genes identified in the blood of multiple sclerosis patients) and datasets GSE17846 and GSE61741(which contains information related to microRNAs taken from multiple sclerosis patients). Genes associated with microRNAs were identified using miRWalk. Common genes from both miRWalk and the dataset GSE21942 were identified and were subjected to STRINGdb for the creation of a protein-protein interaction network and this network was then imported to Cytoscape for identifying the top ten hub genes. The top ten hub genes were subjected to EnrichR for enrichment analysis of genes. In our study, it was found that CTNNB1 is the gene with the highest degree (116).

Neurodegenerative disorders (NDs) are a class of rapidly rising devastating diseases and the reason behind are might be an improper function of related genes or a mutation in a particular gene or even could be autoimmune also. Parkinsons disease (PD), Multiple sclerosis (MS), Huntingtons disease (HD) are some of the NDs, and still, incurable fully. Apart from the similarities in symptoms, there are common genes that express somehow a differential manner in patients of PDs, MSs, and HDs. A total of 1197 differentially expressed genes (DEGs) are obtained by analyzing the chosen datasets. The protein interactions by STRING online tool and degree sorted hubs obtained through a plug-in in Cytoscape; Cyto-Hubba. Among the sorted hubs KRAS, CREB1, PIK3CA, JAK2 are the ones that are not only common to all the studied datasets of NDs but also in other neurological disorders like Alzheimers. The enriched pathways with biological process, molecular function, cellular component, and KEGG pathway details are obtained and analyzed using Enricher. This paper frames that the obtained hub genes could be potential biomarkers also and a need for further drug design for finding a possible cure.

Nowadays, neurological conditions are a major concern as it not only preys on a patient’s health but also is a huge economic burden that is placed on the patient’s family. The diagnosis and treatment of disease sometimes cause methodological limitations. This is mainly common for individuals who have the signs of MS and schizophrenia (SZ). Patients suffering from multiple sclerosis are more likely to develop schizophrenia. Besides, a significant portion of patients who have been diagnosed with Autism Spectrum Disorder (ASD) later acquire the symptoms of Schizophrenia. In this study, we used bioinformatics tools to determine differentially expressed genes (DEGs) in all these diseases, and then we created a protein-protein interaction network using the online software STRING and identified 15 significant genes with the help of Cytohubba a plug-in tool in Cytoscape, the offline software (version3.8.2). We then used a drug-gene interaction database to conduct a drug-gene interaction study of the 15 hub genes and from there we identified 37 FDA-approved drugs. These findings may provide a new and common therapeutic approach for MS, SZ, and ASD therapy.