Huang Zhuo-chun, Shi Yun-ying, Cai Bei, Wang Lan-lan, Wu Yong-kang, Ying Bin-wu, Feng Wei-hua, Hu Chao-jun, Li Yong-zhe
Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu 610041, China.
Sichuan Da Xue Xue Bao Yi Xue Ban. 2009 May;40(3):499-503.
To establish a diagnostic model for systemic lupus erythematosus (SLE) using proteiomic fingerprint techology.
Blood samples were collected from 64 cases of SLE, 30 cases of rheumatoid arthritis (RA), 30 cases of Sjogren's syndrome (SS), 25 cases of systemic sclerosis (SSc), as well as 83 healthy controls. Proteomic spectra of these 232 serum samples were generated by proteomic 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.
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.
This diagnostic model using proteiomic fingerprint techology appears to be a promising tools with high sensitivity and specificity in diagnosis of SLE.
利用蛋白质组指纹技术建立系统性红斑狼疮（SLE）的诊断模型。
收集64例SLE患者、30例类风湿关节炎（RA）患者、30例干燥综合征（SS）患者、25例系统性硬化症（SSc）患者以及83名健康对照者的血样。采用蛋白质组指纹技术生成这232份血清样本的蛋白质组谱。通过一种名为决策增强的机器学习算法建立诊断模型。使用盲法测试集验证诊断模型的敏感性和特异性。
显示SLE患者与对照者之间有60个差异蛋白峰（P<0.05），其中28个在SLE患者中上调，32个下调。该算法识别出一种区分SLE与非SLE的聚类模式，敏感性为91%，特异性为92%。这种有鉴别力的诊断模式能正确识别SLE。在比较SLE与非SLE时，盲法测试的敏感性为78%，特异性为96%。
这种利用蛋白质组指纹技术的诊断模型似乎是一种在SLE诊断中具有高敏感性和特异性的有前景的工具。
