Michelle J Ormseth1,2, Joseph F Solus3,4, Quanhu Sheng3,4, Fei Ye3,4, Qiong Wu3,4, Yan Guo3,4, Annette M Oeser3,4, Ryan M Allen3,4, Kasey C Vickers3,4, C Michael Stein3,4. 1. From the Tennessee Valley Healthcare System, US Department of Veterans Affairs; Department of Medicine, Vanderbilt University Medical Center; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee; Department of Bioinformatics, University of New Mexico, Albuquerque, New Mexico, USA. michelle.ormseth@vumc.org. 2. M.J. Ormseth, MD, Tennessee Valley Healthcare System, US Department of Veterans Affairs, and Department of Medicine, Vanderbilt University Medical Center; J.F. Solus, PhD, Department of Medicine, Vanderbilt University Medical Center; Q. Sheng, PhD, Department of Biostatistics, Vanderbilt University Medical Center; F. Ye, PhD, Department of Biostatistics, Vanderbilt University Medical Center; Q. Wu, PhD, Department of Medicine, Vanderbilt University Medical Center; Y. Guo, PhD, Department of Bioinformatics, University of New Mexico; A.M. Oeser, BA, MLAS, CCRP, Department of Medicine, Vanderbilt University Medical Center; R.M. Allen, PhD, Department of Medicine, Vanderbilt University Medical Center; K.C. Vickers, PhD, Department of Medicine, Vanderbilt University Medical Center; C.M. Stein, MBChB, Department of Medicine, Vanderbilt University Medical Center. michelle.ormseth@vumc.org. 3. From the Tennessee Valley Healthcare System, US Department of Veterans Affairs; Department of Medicine, Vanderbilt University Medical Center; Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee; Department of Bioinformatics, University of New Mexico, Albuquerque, New Mexico, USA. 4. M.J. Ormseth, MD, Tennessee Valley Healthcare System, US Department of Veterans Affairs, and Department of Medicine, Vanderbilt University Medical Center; J.F. Solus, PhD, Department of Medicine, Vanderbilt University Medical Center; Q. Sheng, PhD, Department of Biostatistics, Vanderbilt University Medical Center; F. Ye, PhD, Department of Biostatistics, Vanderbilt University Medical Center; Q. Wu, PhD, Department of Medicine, Vanderbilt University Medical Center; Y. Guo, PhD, Department of Bioinformatics, University of New Mexico; A.M. Oeser, BA, MLAS, CCRP, Department of Medicine, Vanderbilt University Medical Center; R.M. Allen, PhD, Department of Medicine, Vanderbilt University Medical Center; K.C. Vickers, PhD, Department of Medicine, Vanderbilt University Medical Center; C.M. Stein, MBChB, Department of Medicine, Vanderbilt University Medical Center.
Abstract
OBJECTIVE: MicroRNA (miRNA) are short noncoding RNA that regulate genes and are both biomarkers and mediators of disease. We used small RNA (sRNA) sequencing and machine learning methodology to develop an 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 who frequency-matched for age, race, and sex were used for sRNA sequencing. TIGER was used to analyze miRNA. DESeq2 and random forest analyses were used to identify a prioritized list of miRNA differentially expressed in patients with RA. Prioritized miRNA were validated by quantitative PCR, and lasso and logistic regression were used to select the final panel of 6 miRNA 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 patients with SLE (AUC = 0.80) versus controls. Pathway analysis showed upstream regulators and targets of panel miRNA are associated with pathways implicated in RA pathogenesis. CONCLUSION: An 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.
OBJECTIVE: MicroRNA (miRNA) are short noncoding RNA that regulate genes and are both biomarkers and mediators of disease. We used small RNA (sRNA) sequencing and machine learning methodology to develop an 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 who frequency-matched for age, race, and sex were used for sRNA sequencing. TIGER was used to analyze miRNA. DESeq2 and random forest analyses were used to identify a prioritized list of miRNA differentially expressed in patients with RA. Prioritized miRNA were validated by quantitative PCR, and lasso and logistic regression were used to select the final panel of 6 miRNA 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 patients with SLE (AUC = 0.80) versus controls. Pathway analysis showed upstream regulators and targets of panel miRNA are associated with pathways implicated in RA pathogenesis. CONCLUSION: An miRNA panel identified by a bioinformatic approach differentiated between RA or SLEpatients and control subjects. The panel may represent an autoimmunity signature, perhaps related to inflammatory arthritis, which is not dependent on active disease or seropositivity.
Authors: Jason D Arroyo; John R Chevillet; Evan M Kroh; Ingrid K Ruf; Colin C Pritchard; Donald F Gibson; Patrick S Mitchell; Christopher F Bennett; Era L Pogosova-Agadjanyan; Derek L Stirewalt; Jonathan F Tait; Muneesh Tewari Journal: Proc Natl Acad Sci U S A Date: 2011-03-07 Impact factor: 11.205
Authors: Wen Lu; Ran You; Xiaoyi Yuan; Tianshu Yang; Errol L G Samuel; Daniela C Marcano; William K A Sikkema; James M Tour; Antony Rodriguez; Farrah Kheradmand; David B Corry Journal: Nat Immunol Date: 2015-10-05 Impact factor: 25.606
Authors: L Ouboussad; L Hunt; E M A Hensor; J L Nam; N A Barnes; P Emery; M F McDermott; M H Buch Journal: Arthritis Res Ther Date: 2017-12-22 Impact factor: 5.156
Authors: Michelle J Ormseth; Joseph F Solus; Quanhu Sheng; Fei Ye; Haocan Song; Qiong Wu; Yan Guo; Annette M Oeser; Ryan M Allen; Kasey C Vickers; C Michael Stein Journal: ACR Open Rheumatol Date: 2020-01-08