Identification Potential Biomarker Of Rheumatoid Arthritis And Exploration Molecular Functional Mechanisms

Rheumatoid arthritis(RA) is a chronic systemic autoimmune disease, which is characterized by destruction of joint. It cannot be effected a radical cure as well as its high disability rate, which cause serious damage to public health and increase the burden on society. At present, there is no appropriate prediction, diagnostic biomarkers and therapeutic targets of the disease, leading to greatly increase of the disease burden and social medical resources consumption, thus, it is imperative to find out effective RA potential biomarkers. The pathogenesis of RA is not clear, but mainly involved in genetic and environmental factors, what’s more, it has been found that 60% of susceptibility to the disease can explain by the reported RA susceptibility genes. It follows that pay more attention to the susceptibility genes, explore its molecular function mechanism, identify the potential biomarkers has become a top priority on current research of RA. In view of this, we designed the research of identification potential biomarker of rheumatoid arthritis and exploration molecular functional mechanisms, which can mainly divided into two parts: The first part, this study represents the first efforts in identifying RA biomarkers from various functional cells and tissues by using multi-dataset and integrative analysis strategy; The second part, using the integrative experimental analysis strategy to explore RA susceptibility genes molecular function mechanism, excavate potential prediction biomarker which can identify high-risk population of RA.Part 1Objective: In this study, we conducted a multi-dataset analysis, followed by integrative analysis to identify potential shared genes significant in multiple tissues/cells for RA.Methods: The selected 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 included functional annotation clustering analysis, protein-protein interaction(PPI) analysis, gene-based association analysis, and ELISA validation analysis in in-house samples.Results: Our analysis was based on 7 datasets, 2 from synovial tissue, 1 from synovial macrophages, 1 from synovial fluid mononuclear cells(SFMCs), 2 from peripheral blood mononuclear cells(PBMCs), and 1 from peripheral blood cells. Multi-dataset statistical analyses identified 18 shared significant genes, which were mainly involved in the immune response and chemokine signaling pathway. Among the 18 genes, 8 genes(PPBP, PF4, HLA-F, S100A8, RNASEH2 A, 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 presents differential protein levels in in-house case-control plasma samples(P=2.60E-2).Conclusions: This study represented the first effort to identify shared RA markers from different functional cells or tissues. The results taken together suggested that 1 of the shared genes, i.e., PCBP1, is a most promising biomarker for RA.Part 2Objective: We conducted an integrative analysis in various common databases, followed by function experiment analysis to explore functional mechanisms of RA susceptible genes and identify potential prediction biomarker from SNP level.Methods: Based on publicly available datasets and previous studies, we conducted an integrative screening analysis in RA susceptible SNP loci. At first, the SNPs which have a significant P value in RA GWAS, a combination of mi RNA and cis-e QTL effect will be selected. Then the literature search and comprehensive analysis follow up to confirm potential functional loci further. At last AEI experiment and dual luciferase reporter gene assay will be performed to verify the potential functional loci.Results: We identified a total of 166 RA associated SNPs by integrative analysis, followed by literature search and comprehensive analysis to confirm rs907091 as the research target site. The AEI experiment indicated that this SNP can reduce the amount of IKZF3 expression by C > T in vivo, in addition, dual luciferase reporter gene assay in vitro has proved its molecular mechanism is that when C>T, mi R-326 and mi R-330-5p will combine with the region around rs907091 leading to degradation of IKZF3 expression. The decrease of products of IKZF3 can impact the regulation on proliferation, differentiation and apoptosis of T and B lymphocyte, stimulating immune cell to secrete a large number of chemokines and cytokines, leading to systemic inflammation and joint symptoms of RA.Conclusions: By performing integrative analysis of multi-dimensional genetic data, comprehensive literature search and functional experiments, we found that the SNP rs907091 which is located in 3 ’UTR region of IKZF3 on chromosome 17 can regulate the expression of IKZF3 through the mechanism of mi RNA, which influence the pathogenesis of RA. The genotyping information of this SNP could be used to predict RA and identify vulnerable groups of RA.