Screening and bioinformatics of rheumatoid arthritis markers based on GEO chip / 西安交通大学学报(医学版)

ABSTRACT

Objective To perform bioinformatics analysis of the genetic chip data of rheumatoid arthritis (RA) in order to search for the characteristic gene expression profiles. Methods Differential expression analysis of RA Gene chip data in GEO database was performed using GEO2R, and GO and KEGG enrichment analysis of functional annotation and pathway analysis of differentially expressed genes (DEGs) were conducted by DAVID6.8 and R language. Protein-protein interaction (PPI) and target genes acquisition were realized by String-database and software Cytoscape3.7.1. Results The 1 184 DEGs in synovial tissues isolated from the knee joints of RA patients were statistically significant. Among them 664 were up-regulated and 520 were down-regulated. DEGs were enriched to 70 different GOterms, and the most significant terms were signal transduction, plasma membrane and protein binding. DEGs were enriched to 62 different signal pathways, including cytokine-cytokine receptor interaction, osteoclast differentiation, rheumatoid arthritis, Th17 cell differentiation, and IL17 signal pathway. PPI analysis screened out 19 pivotal target genes, namely, NKG7, BCL6, SEMA4D, NFIL3, RAC2, MLIP, SEL1L3, GUSBP11, IGLV1-44, IGLJ3, IGLC1, IGKV1OR2-118, IGKV1OR2-108, IGKC, IGHV4-31, IGHV3-23, IGHM, IGHD and CYAT1. Conclusion Partial DEGs screened out by analyzing the expression profiles are involved in the key links affecting the development of synovial inflammation in RA, which may provide an important theoretical basis for early diagnosis and treatment of this disease and development of targeted drugs.