Identification of key genes associated with multiple sclerosis based on gene expression data from peripheral blood mononuclear cells

Abstract

The aim of this study was to identify the potential key candidate genes of multiple sclerosis (MS) and uncover mechanisms in MS. We combined data from the microarray expression profile of three MS stages and performed bioinformatics analysis. Differentially expressed genes (DEGs) were identified among the distinct stages of MS and healthy controls, and a total of 349 shared DEGs were identified. Gene ontology (GO) and pathway enrichment analyses showed that the DEGs were significantly enriched in the biological processes (BPs) of purine-related metabolic processes and signaling, especially the common DEGs, which were enriched in some immunological processes. Most of the DEGs were enriched in signaling pathways associated with the immune system, some immune diseases and infectious disease pathways. Through a protein–protein interaction (PPI) network analysis and a gene expression regulatory network constructed with MS-related miRNAs, we confirmed FOS, TP53, VEGFA, JUN, HIF1A, RB1, PTGS2, CXCL8, OAS2, NFKBIA and OAS1 as candidate genes of MS. Furthermore , we explored the potential SNPs associated with MS by database mining. In conclusion, this study provides the identified genes, SNPs, biological processes, and cellular pathways associated with MS. The uncovered candidate genes may be potential biomarkers involved in the diagnosis and therapy of MS.

Keywords

• Multiple sclerosis
• Protein protein interaction networks
• miRNA
• SNP
• Enrichment anclysis
• Interaction