Analysis of differentially expressed genes and biological information between rheumatoid arthritis and osteoarthritis based on the GEO database

Abstract

Objective: In the present study, we investigated the relationship between rheumatoid arthritis (RA) and knee osteoarthritis (OA) using bioinformatics, aiming to identify the differentially expressed genes (DEGs) of RA and explore the possible mechanism of RA. Methods: The GSE55584 and GSE153015 microarray datasets for RA and OA gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. The DEGs of the two datasets were obtained by R language processing and analysis. The intersectingDEGs were obtained using the Venny 2.1 platform. Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genome (KEGG) enrichment analyses were performed using the DAVID platform, and the microbubble map was drawn online by importing the microbubble generation platform. All the obtained DEGs and the intersecting DEGs were imported into the STRING platform to obtain a protein‒protein interaction network (PPI) and then into Cytoscape 3.9.1 software to screen core genes (hub genes). Results: A total of 665 DEGs were obtained from the GSE55584 and GSE153015 datasets, including 324 upregulated and 341 downregulated DEGs. GO enrichment analysis showed that the biological processes in which DEGs were mainly enriched included signal transduction, immune response, inflammatory response, adaptive immune response, and G protein-coupled receptor signalling pathway. KEGG enrichment analysis of the DEGs identified the following enriched pathways: cytokine‒cytokine receptor interaction; chemokine signalling pathway; viral protein interaction with cytokines and cytokine receptors; and apoptosis. Ten core genes (hub genes) were screened out, namely, CD3D, CD27, KLRB1, CCL5, GZMB, GZMA, GZMK, GNLY, CD2, and NKG7. Among them, CD3D, CD27, KLRB1, CCL5, and GZMB were most significantly correlated with the occurrence and development of RA. Conclusion: In the present study, bioinformatics analysis provided supporting evidence for the biological process and key genes of RA.