Identification Of Predictive Micro-Rnas Of Progression From Pre-Ra Systemic Autoimmunity To Development Of Ra Using Matched Serum Samples

Background and objectives Recent EULAR recommendations for terminology of rheumatoid arthritis (RA) at-risk groups recognise those with systemic autoimmunity associated with pre-clinical RA, characterised by the presence of ACPA. Identification of predictors of progression from this pre-RA state to the RA syndrome would have clinical utility. MicroRNA (miRNA) are highly conserved small, non-coding RNA that serve as transcriptional negative regulators; with growing evidence that they contribute to the pathogenesis of human diseases, including RA. Our study aimed to identify serum miRNA that predict progression from pre-RA to RA using global profiling. Materials and methods Matched serum samples were used from 12 subjects presenting with CCP positivity only (CCP+) that were followed to the point of development of RA (Very Early RA, VERA). Samples were also obtained from 9 subjects with Early RA (ERA), symptom history of ≤ 12 months; 12 healthy controls formed a control group (HC). TaqMan low density MicroRNA Array Cards was applied to examine the expression of 754 human miRNAs and controls in the 4 cohorts: miRNA with a 4-fold change in expression (down or up-regulation) were selected as candidate biomarkers for RA progression. Heatmaps were plotted using unsupervised hierarchical clustering analysis, permitting separation of cohorts into groups according to miRNA expression levels. A validation analysis of the miRNAs of interest was conducted on a further 12 HC, 12 CCP+ individuals who did not progress and 12 CCP+ individuals that did progress, using custom miRNA array cards. Results After adequate normalisation and data analysis, serum miRNA profiling identified 22 candidate miRNA that were dysregulated when comparing their relative expression from one of the studied cohorts to another. We have identified a four-miRNA signature dysregulated between the matched CCP+ to VERA cohorts. In addition, hierarchical clustering revealed a clear differential miRNA expression between the cohorts. The validation study consists of evaluating expression levels of the 22 miRNA and also 9 miRNA that have been previously implicated in RA (such as miR-155 and miR-146a) using custom array cards in order to confirm their involvement in RA pathogenesis; the results of which are awaited. Conclusions Using a comprehensive MicroRNA array approach this study is a first to use matched pre-RA and VERA serum samples to identify dysregulated miRNA expression as biomarkers predictive of progression along this transition phase. We have identified 4 miRNAs in particular that could be potential diagnostic markers of RA, which we are about to validate further.