Identification of key genes and pathways in Rheumatoid Arthritis gene expression profile by bioinformatics

Objective: The aim of this study was to identify potential key candidate genes and uncover their potential mechanisms in rheumatoid arthritis. Materials and methods: The gene expression profiles of GSE12021, GSE55457, GSE55584 and GSE55235 were downloaded from the gene expression omnibus database, including 45 rheumatoid arthritis and 29 normal samples. The differentially expressed genes between the two types of samples were identified with the Linear Models for Microarray Analysis package using R language. The gene ontology functional and pathway enrichment analyses of differentially-expressed genes were performed using the database for annotation, visualization and integrated discovery software followed by the construction of a protein-protein interaction network. In addition, hub gene identification and gene ontology functional and pathway enrichment analyses of the modules were performed. Results: The differentially expressed genes were mainly involved in immune response, inflammatory response, chemokine-mediated signaling pathway for rheumatoid arthritis patients. The top hub genes such as interleukin 6, jun proto-oncogene, chemokine receptor 5 and epidermal growth factor receptor, were identified from the protein-protein interaction network. Sub-networks revealed hull genes were involved in significant pathways, including chemokine signaling pathway, cytokine-cytokine receptor interaction, tumor necrosis factor signaling pathway. The seed node gene is toll-like receptor 7 and growth arrest and deoxyribonucleic-acid -damage-inducible beta, in the model-1 and model-2 by module analysis, respectively. Conclusion: These hub genes may be used as potential targets for rheumatoid arthritis diagnosis and treatment.