摘要目的 研究SLE患者血清蛋白质谱的变化,从而筛选出特异性的蛋白标记物.方法 采用表面增强激光解吸/离子化飞行时间质谱(SELDI-TOF-MS)技术和弱阳离子交换芯片(CM10)检测72例SLE患者和85例正常人血清蛋白质质谱.用数字表法随机抽取90份标本(40例SLE患者和50例正常人对照者)作为训练组进行系统训练,将筛选出来的差异蛋白峰作为一个标志物组合模式,建立分类树模型(即系统训练过程);用67份血清标本(32例SLE患者和35例正常对照者)作为盲筛组(测试组)验证该模型.结果 在质荷比(M/Z)2000～50 000范围内,共检测到73个蛋白质峰,筛选出差异蛋白质峰15个,以质荷比(M/Z)分别为4001、6305和7356的3个差异蛋白峰建立决策树分类模型,对SLE患者诊断的敏感性为90.0%(36/40),特异性为92.0%(46/50),正确诊断率为91.1%(82/90).用该模型对测试组进行双肓检测,结果 显示敏感性、特异性和正确诊断率分别为87.5%(28/32)、91.4%(32/35)和89.6%(60/67).结论 应用SELDI-TOF-MS技术可以筛选出SLE患者相关的血清蛋白标记物,建立的决策树分类模型可能对SLE患者的诊断具有重要的临床价值.

abstractsObjective To study the changes of serum protein spectrum in patients with systematic lupus erythematosus (SLE) in order to screen specific protein markers. Methods Serum samples from 72 patients with SLE and 85 age- and sex-matched controls were assessed using surface-enhanced laser desorp-tion/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) with weak cation exchange (CM10) pro-rein chip. Forty samples from the patients and 50 control samples were randomly selected to serve as a pre-liminary training set; significantly different protein peaks were automatically chosen for the system training and development of a decision classification tree model. The validity of the model was then challenged with a blind test set (including another 32 samples from patients and 35 from human controls). Results A total of 73 effective protein peaks were detected within the mass/charge ratio (m/z) interval 2000 - 50000, among which, 15 protein peaks significantly differed between patients with SLE and controls (P < 0.01). Three pro-tein peaks with an m/z value of 4001, 6305 and 7356 were automatically chosen as a biomarker pattern in the training set that discriminated patients with SLE from controls with a sensitivity of 90.0% (36/40), speci-ficity of 92.0% (46/50) and accuracy rate of 91.1% (82/90). When the SELDI marker pattern was tested with the blinded test set, it yielded a sensitivity of 87.5% (28/32), specificity of 91.4% (32/35) and accuracy rate of 89.6% (60/67). Conclusions SELDI-TOF-MS protein chip could be used to screen serum protein for SLE, and the decision classification tree model with these biomarkers may favor the diagnosis of SLE.