Bioinformatics analysis of gene expression profiles of Inclusion body myositis

Inclusion body myositis (IBM) is a disease with a poor prognosis and limited treatment options. This study aimed at exploring gene expression profile alterations, investigating the underlying mechanisms and identifying novel targets for IBM. We analysed two microarray datasets (GSE39454 and GSE128470) derived from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to screen out differentially expressed genes (DEGs) between IBM and normal samples. Gene Ontology（GO）function and Kyoto Encyclopedia of Genes and Genomes（KEGG）pathway enrichment analysis were performed using the Database for Annotation, Visualization and Integrated Discovery to identify the pathways and functional annotation of DEGs. Finally, protein-protein interaction (PPI) networks were constructed using STRING and Cytoscape, in order to identify hub genes. A total of 144 upregulated DEGs and one downregulated DEG were identified. The GO enrichment analysis revealed that the immune response was the most significantly enriched term within the DEGs. The KEGG pathway analysis identified 22 significant pathways, the majority of which could be divided into the immune and infectious diseases. Following the construction of PPI networks, ten hub genes with high degrees of connectivity were picked out, namely PTPRC, IRF8, CCR5, VCAM1, HLA-DRA, TYROBP, C1QB, HLA-DRB1, CD74 and CXCL9. Our research hypothesizes that autoimmunity plays an irreplaceable role in the pathogenesis of IBM. The novel DEGs and pathways identified in this study may provide new insight into the underlying mechanisms of IBM at the molecular level.