Efficacy of Integrating a Novel 16-Gene Biomarker Panel and Intelligence Classifiers for Differential Diagnosis of Rheumatoid Arthritis and Osteoarthritis

Nguyen Phuoc Long; Seongoh Park; Nguyen Hoang Anh; Jung Eun Min; Sang Jun Yoon; Hee‐Jung Kim; Tran Diem Nghi; Dong Kyu Lim; Jeong Hill Park; Johan Lim; Sung Won Kwon

doi:10.3390/jcm8010050

journal article Open Access Jan 06, 2019

Efficacy of Integrating a Novel 16-Gene Biomarker Panel and Intelligence Classifiers for Differential Diagnosis of Rheumatoid Arthritis and Osteoarthritis

Nguyen Phuoc Long

Seongoh Park Nguyen Hoang Anh Jung Eun Min Sang Jun Yoon

Hee‐Jung Kim Tran Diem Nghi Dong Kyu Lim Jeong Hill Park Johan Lim Sung Won Kwon

Journal of Clinical Medicine Vol. 8 No. 1 pp. 50 · MDPI AG

View at Publisher Save 10.3390/jcm8010050

Abstract

Introducing novel biomarkers for accurately detecting and differentiating rheumatoid arthritis (RA) and osteoarthritis (OA) using clinical samples is essential. In the current study, we searched for a novel data-driven gene signature of synovial tissues to differentiate RA from OA patients. Fifty-three RA, 41 OA, and 25 normal microarray-based transcriptome samples were utilized. The area under the curve random forests (RF) variable importance measurement was applied to seek the most influential differential genes between RA and OA. Five algorithms including RF, k-nearest neighbors (kNN), support vector machines (SVM), naïve-Bayes, and a tree-based method were employed for the classification. We found a 16-gene signature that could effectively differentiate RA from OA, including TMOD1, POP7, SGCA, KLRD1, ALOX5, RAB22A, ANK3, PTPN3, GZMK, CLU, GZMB, FBXL7, TNFRSF4, IL32, MXRA7, and CD8A. The externally validated accuracy of the RF model was 0.96 (sensitivity = 1.00, specificity = 0.90). Likewise, the accuracy of kNN, SVM, naïve-Bayes, and decision tree was 0.96, 0.96, 0.96, and 0.91, respectively. Functional meta-analysis exhibited the differential pathological processes of RA and OA; suggested promising targets for further mechanistic and therapeutic studies. In conclusion, the proposed genetic signature combined with sophisticated classification methods may improve the diagnosis and management of RA patients.

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Published: Jan 06, 2019
Vol/Issue: 8(1)
Pages: 50
License: View

Authors

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Nguyen Phuoc Long