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目的应用表面增强激光解析离子化-飞行时间质谱技术(surface-enhanced laser desorption ioni zation/time of flight mass spectrometry,SELDI-TOF-MS)和蛋白质芯片筛选强直性脊柱炎(ankylosing spondylitis,AS)患者血清特异性标志物,用于疾病诊断、评估及预测病情进展。方法采用SELDI-TOF-MS技术和弱阳离子交换(weak cation exchange)芯片检测2008年4月至2009年1月山西医科大学第二医院风湿免疫科收治的69例AS患者及12名健康对照者、10例类风湿关节炎(rheumatoid arthritis,RA)患者血清蛋白质表达,进一步将AS患者分为活动期与非活动期,中轴关节受累及中轴、外周关节均受累,HLA-B27阳性与阴性组,比较不同分组之间患者血清蛋白质指纹图谱,采用SELDI质谱仪自带的Biomarker Wizard和Biomarker Pattern软件筛选,初步建立疾病诊断预测模型。结果由8085、2640和2932建立的诊断预测模型Ⅰ诊断AS的敏感度为94.23%,特异度为100%。由3677、3880、2539、3159和3242建立的诊断预测模型Ⅱ判断病情活动的敏感度为98.11%,特异度为100%。由4700、8687和18538建立的诊断预测模型Ⅲ预测AS同时有中轴及外周关节受累的敏感度为80.00%,特异度为82.35%。由10259、7972、2048、2154和2954建立的诊断模型Ⅳ区分AS和RA的敏感度为100%(69/69),特异度为100%(10/10)。结论通过SELDI-TOF-MS技术建立的血清蛋白质指纹图谱可以筛选AS患者血清中的特异性蛋白质标志物,有望成为诊断疾病及评估病情的一种初筛平台。
Objective To screen the serum of patients with ankylosing spondylitis (AS) by surface-enhanced laser desorption ionization / time of flight mass spectrometry (SELDI-TOF-MS) Specific markers for disease diagnosis, assessment and prediction of disease progression. Methods Seventy-nine patients with AS and 12 healthy controls were enrolled in the Department of Rheumatology, the Second Hospital of Shanxi Medical University from April 2008 to January 2009 with SELDI-TOF-MS and weak cation exchange. 10 cases of rheumatoid arthritis (rheumatoid arthritis, RA) serum protein expression, further AS patients were divided into active and inactive, central axis involvement and central axis, peripheral joints are involved, HLA-B27 positive and negative groups The serum protein fingerprints of patients were compared between different groups. The SELDI mass spectrometer was used to screen the Biomarker Wizard and Biomarker Pattern software to establish the disease prediction model. Results The diagnostic predictive model I established by 8085, 2640 and 2932 had a sensitivity of 94.23% and a specificity of 100% for the diagnosis of AS. The diagnostic prediction model Ⅱ established by 3677, 3880, 2539, 3159 and 3242 has a sensitivity of 98.11% and a specificity of 100% for judging the activity of the disease. The diagnostic predictive model Ⅲ established by 4700, 8887 and 18538 predicts that the sensitivity and the specificity of central axis and peripheral joint involvement in AS are 80.00% and 82.35% respectively. The diagnostic model established by 10259, 7972, 2048, 2154, and 2954 distinguishes AS from RA with a sensitivity of 100% (69/69) and a specificity of 100% (10/10). Conclusion Serum protein fingerprinting established by SELDI-TOF-MS can screen specific protein markers in serum of patients with AS, which is expected to become a screening platform for diagnosis and evaluation of disease.