W Liu1, X Li, F Ding, Y Li. 1. Department of Rheumatology, Shandong University Qilu Hospital, Jinan, P.R. China.
Abstract
OBJECTIVES: No satisfactory biomarkers are currently available to screen for rheumatoid arthritis (RA). We have developed and evaluated surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) for detection and analysis of multiple proteins for distinguishing individuals with RA from control individuals. METHODS: A total of 156 serum samples from 90 RA patients, 30 patients with ankylosing spondylitis (AS), and 36 healthy individuals were examined by SELDI technology. Spectral data were analysed by the support vector machine (SVM) approach and potential biomarkers were chosen for system training and were used to construct a diagnostic model. RESULTS: Pattern 1, consisting of four protein peaks with m/z values of 3899, 4594, 7566, and 13,842, distinguished RA from the healthy samples with sensitivity of 90.0% and a specificity of 91.7%. Pattern 2, consisting of m/z peaks 4287 and 6471, distinguished RA from AS with a sensitivity of 86.7% and a specificity of 85.0%. CONCLUSION: The combination of SELDI-TOF MS and SVM could facilitate the discovery of better biomarkers for RA and also provide a useful tool for molecular diagnosis in the future.
OBJECTIVES: No satisfactory biomarkers are currently available to screen for rheumatoid arthritis (RA). We have developed and evaluated surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) for detection and analysis of multiple proteins for distinguishing individuals with RA from control individuals. METHODS: A total of 156 serum samples from 90 RApatients, 30 patients with ankylosing spondylitis (AS), and 36 healthy individuals were examined by SELDI technology. Spectral data were analysed by the support vector machine (SVM) approach and potential biomarkers were chosen for system training and were used to construct a diagnostic model. RESULTS: Pattern 1, consisting of four protein peaks with m/z values of 3899, 4594, 7566, and 13,842, distinguished RA from the healthy samples with sensitivity of 90.0% and a specificity of 91.7%. Pattern 2, consisting of m/z peaks 4287 and 6471, distinguished RA from AS with a sensitivity of 86.7% and a specificity of 85.0%. CONCLUSION: The combination of SELDI-TOF MS and SVM could facilitate the discovery of better biomarkers for RA and also provide a useful tool for molecular diagnosis in the future.
Authors: Terrance P O'Hanlon; Zhuoyan Li; Lu Gan; Mark F Gourley; Lisa G Rider; Frederick W Miller Journal: Arthritis Res Ther Date: 2011-11-01 Impact factor: 5.156