Literature DB >> 27812736

Integrative analysis for identification of shared markers from various functional cells/tissues for rheumatoid arthritis.

Wei Xia1,2, Jian Wu3, Fei-Yan Deng1,2, Long-Fei Wu1,2, Yong-Hong Zhang2, Yu-Fan Guo4, Shu-Feng Lei5,6.   

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.

Entities:  

Keywords:  Integrative analysis; Multi-dataset analysis; Rheumatoid arthritis; Shared genetic marker

Mesh:

Substances:

Year:  2016        PMID: 27812736     DOI: 10.1007/s00251-016-0956-4

Source DB:  PubMed          Journal:  Immunogenetics        ISSN: 0093-7711            Impact factor:   2.846


  51 in total

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