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作 者:黄卓春[1] 石运莹[2] 蔡蓓[1] 王兰兰[1] 武永康[1] 应斌武[1] 冯伟华[1] 胡朝军[3] 李永哲[3]
机构地区:[1]四川大学华西医院实验医学科,成都610041 [2]四川大学华西医院肾脏内科,成都610041 [3]中国医学科学院北京协和医院免疫内科
出 处:《四川大学学报(医学版)》2009年第3期499-503,共5页Journal of Sichuan University(Medical Sciences)
基 金:国家自然科学基金(批准号30670819;30772051)资助
摘 要:目的运用蛋白质指纹图谱技术筛选系统性红斑狼疮(SLE)患者血清中的特异性蛋白标志物,建立SLE疾病相关的蛋白质组学诊断模型。方法联用弱阳离子磁珠与蛋白质芯片阅读仪绘制64例SLE患者组及168例对照组的血清蛋白质指纹图谱,用Biomarker Patterns Software5.0(BPS)软件筛选特异性的血清蛋白标志物并建立SLE诊断模型。结果在SLE患者组和对照组之间找到60个差异蛋白峰(P<0.05),其中28个蛋白峰在SLE患者表达增高,32个蛋白峰表达降低。由BPS软件筛选的4个蛋白标志物(质荷比为3376.02、4070.09、7770.45、28045.10)建立的诊断模型能很好的把SLE患者与其他自身免疫性疾病和健康对照者区分出来,经过盲法验证,其对SLE的诊断敏感性为78%,特异性为96%。结论采用蛋白质指纹图谱技术能筛查识别出与SLE疾病相关的特异性血清蛋白标志物,由4个质荷比分别为3376.02、4070.09、7770.45和28045.1的SLE特异性血清蛋白标志物建立的SLE疾病诊断模型具有高敏感性及特异性。Objective To estahlish a diagnostic model for systemic lupus erythematosus (SLE) using proteiomic fingerprint techology. Methods Blood samples were collected from 64 cases of SLE, 30 cases of rheumatoid arthritis(RA) ,30 cases of Sjogren'ssyndrome(SS), 25 cases of systemic sclerosis (SSc), as well as 83 healthy controls. Proteomic spectra of these 232 serum samples were generated by proteiomic fingerprint technology. Diagnostic model was established by a machine learning algorithm called decision boosting. The sensitivity and specificity of the diagnostic model was validated with a blinded testing set. Results Sixty differential protein peaks(P〈0. 05) between SLE and control subjects were indicated, 28 of them were up regulated and 32 were down regulated in SLE patients. The algorithm identified a cluster pattern segregating SLE from non-SLE with sensitivity of 91% and specificity of 92%. The discriminatory diagnostic pattern correctly identified SLE. A sensitivity of 78% and specificity of 96% for the blinded test were obtained when comparing SLE vs non-SLE. Conclusion This diagnostic model using proteiomic fingerprint techology appears to be a promising tools with high sensitivity and specificity in diagnosis of SLE.
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