Background/Purpose: MicroRNAs (miRNAs) are short non-coding RNAs that regulate genes and have utility as disease biomarkers. Use of small RNA (sRNA) sequencing along with unbiased bioinformatics methods has the capacity to reveal new miRNA markers of disease. The objective of this study was to use sRNA sequencing and machine learning methodology to determine a miRNA signature panel capable of reliably differentiating patients with rheumatoid arthritis (RA) from control subjects, and from patients with another autoimmune disease.

Methods: Plasma samples from 167 patients with RA and 91 control subjects frequency-matched for age, race and sex were used for RNA extraction and sequencing library preparation. Sequencing was performed using Illumina NextSeq500. Our in-house pipeline, TIGER, was used to quantify miRNAs. DESeq2 and random forest analyses were used to develop a prioritized list of differentially expressed miRNAs in RA compared to control subjects. Each of the miRNAs was validated by PCR in the same plasma samples. Using the PCR-based plasma concentrations of the miRNAs, we used lasso regression to select a final miRNA panel to distinguish between RA and control subjects. This panel was validated in a separate cohort of 32 patients with RA and 32 control subjects and in 12 patients with SLE. Panel efficacy was assessed by area under the receiver operative characteristic curve (AUC) analyses.

Results: Among the top 15 miRNAs selected separately by DESeq2 and random forest analyses, 12 miRNAs overlapped using both methodologies, thus a total of 18 miRNAs were selected for PCR validation. Using the PCR-based concentrations of the 18 miRNAs, a final panel that included miR-22-3p, miR-24-3p, miR-96-5p, miR-134-5p, miR-140-3p, and miR-627-5p was selected. The panel had an AUC=0.81 for differentiating RA and control subjects, and it was robust among those with seronegative RA (AUC=0.84), among patients whose RA was in remission (AUC=0.85), in a separate RA validation cohort (AUC=0.76), and among patients with SLE compared to control subjects (AUC=0.97). Three of the miRNAs had weak association with disease activity in RA by DAS28 score (miR-24-3p: Rho= -0.16, P=0.04; miR-96-5p: Rho= 0.16, P=0.04; miR-140-3p: Rho=-0.16, P=0.05), and none were associated with disease activity in SLE (SLEDAI).

Conclusion: A miRNA panel identified by unbiased bioinformatics approaches was able to differentiate between patients with RA, SLE and control subjects. The panel may represent an autoimmunity signature which is common to both RA and SLE patients and which is not dependent on active disease or seropositivity. Further studies will be necessary to confirm these findings.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/development-and-validation-of-a-microrna-panel-to-differentiate-between-patients-with-rheumatoid-arthritis-systemic-lupus-erythematosus-and-control-subjects/