Luyuan Sun1, Yimin Chai. 1. Department of Orthopaedic Surgery, Sixth People's Hospital, Jiaotong University, Shanghai, China.
Abstract
BACKGROUND: Rheumatoid arthritis (RA) is a chronic, systemic inflammatory disorder that may affect many tissues and organs, but principally attacks flexible (synovial) joints. AIMS: Our aim is to explore the change of gene expression profile in patients with RA, and investigate the underlying mechanism of the pathogenesis and progression of RA. METHODS: We downloaded the dataset GSE2053 from Gene Expression Omnibus database and screened the differentially expressed genes by analyzing the profiles between RA and normal cells with bioinformatics methods. Furthermore, Gene Ontology (GO) function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to screen GO and the significantly changed signaling pathways in RA cells with the Database for Annotation, Visualization and Integrated Discovery (DAVID). RESULTS: By bioinformatics methods, we obtained the metabolic pathway changed in the cells of patients with RA, and explored small molecule drugs that can restore these changes. CONCLUSIONS: These results may provide a new approach for explore the pathogenesis of RA and a new breakthrough in the medical treatment of patients with RA.
BACKGROUND:Rheumatoid arthritis (RA) is a chronic, systemic inflammatory disorder that may affect many tissues and organs, but principally attacks flexible (synovial) joints. AIMS: Our aim is to explore the change of gene expression profile in patients with RA, and investigate the underlying mechanism of the pathogenesis and progression of RA. METHODS: We downloaded the dataset GSE2053 from Gene Expression Omnibus database and screened the differentially expressed genes by analyzing the profiles between RA and normal cells with bioinformatics methods. Furthermore, Gene Ontology (GO) function analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were used to screen GO and the significantly changed signaling pathways in RA cells with the Database for Annotation, Visualization and Integrated Discovery (DAVID). RESULTS: By bioinformatics methods, we obtained the metabolic pathway changed in the cells of patients with RA, and explored small molecule drugs that can restore these changes. CONCLUSIONS: These results may provide a new approach for explore the pathogenesis of RA and a new breakthrough in the medical treatment of patients with RA.