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Development and Validation of a microRNA Panel to Differentiate between Patients with Rheumatoid Arthritis or Systemic Lupus Erythematosus and Controls

Author

Ormseth MJ1, Solus JF1, Sheng Q1, Ye F1, Wu Q1, Guo Y1, Oeser AM1, Allen RM1, Vickers KC1, Stein CM1. J Rheumatol. 2019 May 15. pii: jrheum.181029. doi: 10.3899/jrheum.181029. [Epub ahead of print]

Author Information

1 From the Tennessee Valley Healthcare System, U.S. Department of Veterans Affairs, Nashville, TN; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN; Department of Bioinformatics, University of New Mexico, Albuquerque, NM, USA, Funding: Veterans Health Administration CDA IK2CX001269, ArthritisFoundation Delivering on Discovery grant, Alpha Omicron Pi, NIH Grants: P60 AR056116, P01 HL116263 and CTSA award UL1TR000445 from the National Center for Advancing Translational Sciences. Its contents are solely the responsibility of the authors and do not necessarily represent official views of the National Center for Advancing Translational Sciences or the National Institutes of Health. Address for correspondence: Michelle J Ormseth, MD, 1161 21st Avenue South, T-3113 MCN, Nashville, TN 37232-2681, Telephone: 615-322-4746, Fax: 615-322-6248, Email: michelle.ormseth@vumc.org.

Abstract

OBJECTIVE: 

MicroRNAs (miRNAs) are short non-coding RNAs that regulate genes and are both biomarkers and mediators of disease. We used small RNA (sRNA) sequencing and machine learning methodology to develop a miRNA panel to reliably differentiate between rheumatoid arthritis (RA) or systemic lupus erythematosus (SLE) and control subjects.

METHODS: 

Plasma samples from 167 RA and 91 control subjects frequency-matched for age, race and sex were used for sRNA sequencing. TIGER was used to analyze miRNAs. DESeq2 and random forest analyses were used to identify a prioritized list of miRNAs differentially expressed in patients with RA. Prioritized miRNAs were validated by quantitative PCR, and lasso and logistic regression were used to select the final panel of six miRNAs that best differentiated RA from controls. The panel was validated in a separate cohort of 12 SLE, 32 RA and 32 control subjects. Panel efficacy was assessed by area under the receiver operative characteristic curve (AUC) analyses.

RESULTS: 

The final panel included miR-22-3p, miR-24-3p, miR-96-5p, miR-134-5p, miR-140-3p, and miR- 627-5p. The panel differentiated RA from control subjects in discovery (AUC=0.81) and validation cohorts (AUC=0.71), seronegative RA (AUC=0.84), RA remission (AUC=0.85), and SLE patients (AUC=0.80) versus controls. Pathway analysis showed upstream regulators and targets of panel miRNAs are associated with pathways implicated in RA pathogenesis.

CONCLUSION: 

A miRNA panel identified by a bioinformatic approach differentiated between RA or SLE patients and control subjects. The panel may represent an autoimmunity signature, perhaps related to inflammatory arthritis, which is not dependent on active disease or seropositivity.