应用SELDI-TOF-MS技术分析VKC泪液的差异表达蛋白

Detection of Tears Differential Expression Protein by SELDI-TOF-MS Technology in Vernal Kerato Conjunctivits(VKC)

目的:探讨用蛋白质芯片技术筛选春季卡他性结膜炎(vernal keratoconjunctivis,VKC)患者泪液中蛋白质表达谱,寻找泪液中的标志性蛋白。方法:采用表面增强激光解离飞行时间质谱技术(surface-enhanced la-ser desorption/ionization time of flight mass spectrometry,SELDI-TOF-MS),运用CM10蛋白质芯片检测66例VKC患者和62例正常对照组泪液中蛋白质谱,获得的蛋白质谱采用Biomarker Wizard软件分析,初步筛选蛋白质峰,结合生物信息学的支持向量机(support vector machines,SVM)方法建立并测试VKC患者泪液中的蛋白质指纹图谱模型。结果:在芯片上捕获到145种蛋白质,用质谱仪筛选出VKC患者与正常对照组相比的23种差异蛋白,从中再次筛选出3种蛋白质组成VKC的蛋白质谱最优化模型,VKC患者泪液中质荷比(m/z)分别为2024.3,6630.2和8598.9的3种蛋白质表达上调。模型经三倍交叉验证后用盲法测定,其敏感性和特异性分别为90.91%和93.55%,阳性预测值为93.75%。结论:蛋白质芯片技术可快速、有效地筛选出VKC患者泪液差异蛋白,结合SVM可建立一个由3种蛋白质组成的蛋白质指纹图谱模型,可对VKC做很好的诊断预测,对这3种蛋白质尤其是m/z为2024.8的蛋白质进行研究,有助于VKC病因学进展及诊断标记物的发现。

Objective To screen out tears protein profiling of VKC by SELDI-TOF-MS for discovering the discriminatory pro- teins. Methods sixty six men with VKC and sixty two healthy men were detected by CM10 chip. Protein chip reader and biomarker wizard software from ciphergen inc were combined with a bioinformatics method （support vector machines, SVM ） to analyze protein finger-printing. Results Three proteins which were obviously different between the VKC group and the control. They were up-regula- ted. To setup an analysis model by SVM using the 3 proteins could successfully distinguish between VKC and the normal control. The corresponding sensitivity, specificity and positive predict value were 90. 91%, 93.55%, 93.75%, resectively. Conclusion Using protein chip technology can screen out tears discriminatory proteins quickly and efficiently. Combined with SVM, an optimal fingerprint- ing model has been set up, which can easily predict VKC. In disease state of VKC, there are significant variations which consists of four proteins. To investigate those three discriminatory proteins, especially the protein m/z 2024.3 may be of benefit to etiologic study and the development of biomarkers for VKC.