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Urinary Proteome Profile Predictive of Disease Activity in Rheumatoid Arthritis
- Urinary Proteome Profile Predictive of Disease Activity in Rheumatoid Arthritis
- Kang, Min Jueng; Park, Yune-Jung; You, Sungyong; Yoo, Seung-Ah; Choi, Susanna; Kim, Dong-Ho; Cho, Chul-Soo; Yi, Eugene C.; Hwang, Daehee; Kim, Wan-Uk
- DGIST Authors
- Hwang, Daehee
- Issue Date
- Journal of Proteome Research, 13(11), 5206-5217
- Article Type
- Current serum biomarkers for rheumatoid arthritis (RA) are not highly sensitive or specific to changes of disease activities. Thus, other complementary biomarkers have been needed to improve assessment of RA activities. In many diseases, urine has been studied as a window to provide complementary information to serum measures. Here, we conducted quantitative urinary proteome profiling using liquid chromatographytandem mass spectrometry (LCMS/MS) and identified 134 differentially expressed proteins (DEPs) between RA and osteoarthritis (OA) urine samples. By integrating the DEPs with gene expression profiles in joints and mononuclear cells, we initially selected 12 biomarker candidates related to joint pathology and then tested their altered expression in independent RA and OA samples using enzyme-linked immunosorbent assay. Of the initial candidates, we selected four DEPs as final candidates that were abundant in RA patients and consistent with those observed in LCMS/MS analysis. Among them, we further focused on urinary soluble CD14 (sCD14) and examined its diagnostic value and association with disease activity. Urinary sCD14 had a diagnostic value comparable to conventional serum measures and an even higher predictive power for disease activity when combined with serum C-reactive protein. Thus, our urinary proteome provides a diagnostic window complementary to current serum parameters for the disease activity of RA.
- American Chemical Society
- Related Researcher
Systems Biology and Medicine Lab
Multilayered spatiotemporal networks; Regulatory motifs or pathways; Metabolite-protein networks; Network stochasticity; Proteomics and informatics
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- Department of New BiologySystems Biology and Medicine Lab1. Journal Articles
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