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Integrative analysis for identification of shared markers from various functional cells/tissues for rheumatoid arthritis.
Xia, Wei; Wu, Jian; Deng, Fei-Yan; Wu, Long-Fei; Zhang, Yong-Hong; Guo, Yu-Fan; Lei, Shu-Feng.
Afiliação
  • Xia W; Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China.
  • Wu J; Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China.
  • Deng FY; Department of Rheumatology, the First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, 215000, People's Republic of China.
  • Wu LF; Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China.
  • Zhang YH; Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China.
  • Guo YF; Center for Genetic Epidemiology and Genomics, School of Public Health, Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China.
  • Lei SF; Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases, Soochow University, Suzhou, Jiangsu, 215123, People's Republic of China.
Immunogenetics ; 69(2): 77-86, 2017 Feb.
Article em En | MEDLINE | ID: mdl-27812736
ABSTRACT
Rheumatoid arthritis (RA) is a systemic autoimmune disease. So far, it is unclear whether there exist common RA-related genes shared in different tissues/cells. In this study, we conducted an integrative analysis on multiple datasets to identify potential shared genes that are significant in multiple tissues/cells for RA. Seven microarray gene expression datasets representing various RA-related tissues/cells were downloaded from the Gene Expression Omnibus (GEO). Statistical analyses, testing both marginal and joint effects, were conducted to identify significant genes shared in various samples. Followed-up analyses were conducted on functional annotation clustering analysis, protein-protein interaction (PPI) analysis, gene-based association analysis, and ELISA validation analysis in in-house samples. We identified 18 shared significant genes, which were mainly involved in the immune response and chemokine signaling pathway. Among the 18 genes, eight genes (PPBP, PF4, HLA-F, S100A8, RNASEH2A, P2RY6, JAG2, and PCBP1) interact with known RA genes. Two genes (HLA-F and PCBP1) are significant in gene-based association analysis (P = 1.03E-31, P = 1.30E-2, respectively). Additionally, PCBP1 also showed differential protein expression levels in in-house case-control plasma samples (P = 2.60E-2). This study represented the first effort to identify shared RA markers from different functional cells or tissues. The results suggested that one of the shared genes, i.e., PCBP1, is a promising biomarker for RA.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Artrite Reumatoide / Biomarcadores / Biologia Computacional / Perfilação da Expressão Gênica / Polimorfismo de Nucleotídeo Único / Redes Reguladoras de Genes Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Artrite Reumatoide / Biomarcadores / Biologia Computacional / Perfilação da Expressão Gênica / Polimorfismo de Nucleotídeo Único / Redes Reguladoras de Genes Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Ano de publicação: 2017 Tipo de documento: Article