原发免疫性血小板减少症血清标志物的筛选

Screening of Serum Markers in Primary Immune Thrombocytopenia

目的:利用弱阳离子磁珠联合MALDI-TOF-MS分析ITP患者和健康对照血清蛋白质表达谱,鉴定差异表达蛋白,建立ITP的诊断模型,探索ITP的发病机制。方法:选择ITP患者40例和健康对照40例,应用美国布鲁克.道尔顿(Bruker Daltonics)公司生产的WCX-MB(Weak cation exchange nanometer magnetic beads)捕获血清蛋白质组分,用Autoflex II基质辅助激光解吸电离飞行时间质谱仪MALDI-TOF-MS检测各样品的蛋白质质谱,再利用Bruker Daltonics公司的Cliprotools TM2.2软件进行数据分析,筛选差异蛋白质分子并建立ITP诊断模型,并选取20例ITP血清和20例健康对照血清对产生的模型进行分类验证和交叉验证。结果:40例ITP建模血清标本与40例健康对照建模血清标本经Clinprot系统检测蛋白质质谱,应用Cliprotools TM2.2软件进行数据分析,在分子量700-10000 Da范围内,可检测到55个差异蛋白质表达峰,其中有统计学意义的差异蛋白质峰19个(P<0.05),在ITP组表达上调的蛋白质有5个,下调的蛋白质有14个。软件自动将区分疾病组与对照组能力最强的10个质谱峰建成了一个基于SNN(Supervised Neural Network Algorithm)算法的诊断模型,建模组判断ITP的预期敏感性为100%,预期特异性为100%。应用建立的SNN诊断模型对验证组进行分类验证,20例ITP病例及20例健康对照均被全部正确识别;交叉验证对ITP患者和健康对照检出的正确率为100%。结论:应用具有高通量及良好重复性Clin Prot系统建立的ITP SNN模型由10个显著差异蛋白质峰构成,能有效区分ITP患者与正常对照,而且敏感性和特异性较高。

Objective: To analyze the serum protein fingerprints of immune thrombocytopenia( ITP) patients and healthy controls by using w eak cation exchange nanometer magnetic beads and M ALDI- TOF- M AS technology,to identify the proteins w ith different expression,to establish the diagnostic model for ITP and to explore the pathogenesis of ITP. Methods: A total of 40 patients w ith ITP and 40 healthy controls w ere selected,the serum protein components w ere captured by using w eak cation exchange nanaometer magnetic beads,the protein spectra of all specimens w ere detected by Autoflex II matrix assisted laser desorption / ionization time of flight mass spectrometry( M ALDI- TOF- M S) and then the data w ere analyzed by Cliprotools TM2. 2 softw are,by w hich the distinct protein molecules w ere screened to set up ITP diagnostic model. To identify the established model,the sera of 20 ITP patients and 20 healthy controls w ere selected to make category and cross validations. Results: The detection of Clinprot system and the analysis of Cliprotools TM2. 2softw are show ed that about 55 protein peaks w ere detected w ith the range of 700 Da to 10 000 Da of molecular w eight in the protein spectrum of serum speciments from 40 ITP patients and 40 healthy controls. Compared w ith healthy controls,19 protein expression peaks w ith statistically significant difference w ere found in ITP patients( P < 0. 05),among them 5expressions w ere up-regulated and 14 expressions w ere dow n-regulated. The diagnostic model on basis of Supervised Neural Netw ork Algorithm( SNN) w as established through 10 M S peaks w ith strongest capability in ITP group and control group automatically distinguished by softw are,and it is expected that the sensitivity of model group reached to 100%,and the specificity to 100%. The category validation show ed that this diagnostic model correctly identificed all 20 ITP patients and20 healthy controls,and in cross validation,the model sensitivity w as 100% and the specificity was 100%. Conclusion: The ITP SNN model ertablished by using Chin Prot System w ith high flax and good repetition is composed of 10 protein peaks w ith significant difference,this model can effectively distinguish ITP patients and healthy controls.