Identification of differentially expressed genes and signaling pathways in rheumatoid arthritis by integrated bioinformatics analysis

Abstract

<jats:title>Abstract</jats:title> <jats:p>Background The disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify key genes and pathways involved in RA utilizing integrated bioinformatics analysis and uncover underlying molecular mechanisms. Materials and methods The expression profiles of GSE55235, GSE55457, GSE55584 and GSE77298 were downloaded from the Gene Expression Omnibus database, which contained 76 synovial membrane samples, including 49 RA samples and 27 controls. The microarray datasets were consolidated and differentially expressed genes (DEGs) were acquired and further analyzed by bioinformatics techniques. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R (version 3.6.1), respectively. The protein-protein interaction (PPI) networks of DEGs were developed utilizing the STRING database. Results A total of 828 DEGs were recognized, with 758 up-regulated and 70 down-regulated. GO and KEGG pathway analyses demonstrated that these DEGs focused primarily on multifactorial binding, transcription activity, cytokin-cytokin receptor interaction and relevant signaling pathways. The 30 most firmly related genes among DEGs were identified from the PPI network. Conclusion This study shows that screening for DEGs and pathways utilizing integrated bioinformatics analyses could aid in the comprehension of the molecular mechanisms involved in RA development. In addition, our study provides valuable data for the effective prevention, diagnosis, treatment and rehabilitation of RA patients as well as providing potential targets for the treatment of RA.</jats:p>