【摘要】 目的分析肺癌与肺部良性疾病及正常人血浆蛋白质指纹图谱的变化,建立肺癌血浆标志物诊断模型。方法应用蛋白质指纹图谱(SELDI-TOF-Ms)技术检测108例肺癌患者、40例肺部良性疾病患者和22例正常人血浆标本,采用层次聚类分析和主成分分析建立决策树模型,应用该模型盲筛21例肺部良性疾病和47例Ⅰ期肺癌。结果筛选到23个差异蛋白峰(P<0.001)。盲筛分析显示决策树模型诊断敏感性和特异性分别为72.34%和71.43%,阳性预测值和阴性预测值分别为85.0%和78.95%,诊断正确性为72.06%。结论应用SELDI-TOF-Ms技术初步建立的蛋白质模型为肺癌的早期诊断提供了新的技术平台。更多还原

【Abstract】 Objective To explore the changes of proteomic spectra from plasma of patients with lung cancer or benign lung diseases and health controls in order to establish a primary diagnosis model of lung cancer. Methods The proteomic spectra from plasma of 108 patients with lung cancer,40 patients with benign lung diseases and 22 healthy individuals were analysed by surface-enhanced laser desorption/ionization time of flight mass spectrometry(SELDI-TOF-MS).The best decision tree model was established by cluster analysis and principal component analysis.Then the model was blindly validated by the protein of 21 patients with lung benign diseases and 47 patients with stage I lung cancer. ResultsTwenty-three significantly differentially expressed protein peaks were successfully detected(P<0.001).Blinded validation suggested that the accuracy for diagnosing lung cancer was 72.06%,the sensitivity and specificity were 72.34% and 71.43%,respectively,and the positive predictive value and negative predictive value were 85.0% and 78.95%,respectively. Conclusion SELDI-TOF-MS protein chip technology provides a new tool for the early diagnosis of lung cancer.更多还原