论文部分内容阅读
目的采用蛋白质组学新技术筛选冠心病不稳定型心绞痛(unstable angina,UA)患者血清蛋白标志分子,建立诊断不稳定心绞痛的蛋白分类模型。方法冠心病不稳定型心绞痛患者48例,男28例,女20例;正常健康人60例,患者出现症状24h内采血,分离血清。样本分为两组:第一组为训练组,包括UA患者和正常健康人各30例,性别年龄相当;第二组为盲法分析组,包括剩余18例UA患者和26例正常健康人。利用(surface enhanced laser desorption/ionization time of flight mass spectrometry,SELDI-TOF-MS)技术对血清样本进行蛋白质谱分析。采用蛋白飞行质谱仪(PBSⅡ-C型)对结合在WCX2芯片上的血清蛋白进行读取分析。采用Ciphergen Proteinchip 3.1软件分析分组数据及相关性,Biomarker Wizard软件对不同组相同质荷比的蛋白含量进行t检验,P<0.01时具有统计学意义,用Biomarker Patterns System建立分类树模型。结果UA患者与正常人血清蛋白组比较后发现了25个表达差异蛋白,其中13个蛋白在UA患者血清中高表达,12个低表达。软件分析系统利用上述蛋白建立了一个分类模式,在训练组中敏感性和特异性均为96.6%。盲法分析显示其对UA患者的诊断敏感性为94.4%,特异性为100%,阳性预测值为100%,阴性预测值96.3%。结论SELDI技术能够有效快速地从血清中筛选出UA患者相关的标志蛋白分子,利用蛋白模式分类方法对冠心病患者进行风险评估和冠心病的筛查是一个比较好的方法。
OBJECTIVE: To screen serum protein markers of unstable angina (UA) patients by proteomics and to establish a protein classification model for the diagnosis of unstable angina pectoris. Methods 48 cases of unstable angina pectoris patients, 28 males and 20 females; 60 normal healthy subjects, patients with symptoms within 24h blood collection, serum separation. The samples were divided into two groups: the first group consisted of 30 participants in the training group, including UA patients and normal healthy people, with the same gender age; the second group consisted of blind analysis group including the remaining 18 UA patients and 26 healthy controls. Serum samples were subjected to protein profiling using a surface enhanced laser desorption / ionization time of flight mass spectrometry (SELDI-TOF-MS) technique. Serum proteins bound to the WCX2 chip were read and analyzed using a protein flight mass spectrometer (PBS II-C). Grouping data and correlation were analyzed using Ciphergen Proteinchip 3.1 software. Biomarker Wizard software performed t-test on the protein content of different groups with the same mass-to-charge ratio. Statistical significance was found at P <0.01. The classification tree model was established by Biomarker Patterns System. Results There were 25 differentially expressed proteins in serum of UA patients and normal controls. Thirteen of them were highly expressed in sera of UA patients and 12 were low in UA patients. Software analysis system using the above proteins to establish a classification model, the sensitivity and specificity of the training group were 96.6%. Blinded analysis showed a sensitivity of 94.4%, a specificity of 100%, a positive predictive value of 100%, and a negative predictive value of 96.3% for UA patients. Conclusion The SELDI technique can screen out the related marker proteins in serum of patients with UA effectively and quickly. It is a good method to evaluate the risk of coronary heart disease and screening coronary heart disease by protein pattern classification.