CAJ | 학술논문

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.
목적 분석폐암여폐부량성질병급정상인혈장단백질지문도보적변화,건립폐암혈장표지물진단모형.방법 응용단백질지문도보(SELDI-TOF-Ms)기술검측108례폐암환자、40례폐부량성질병환자화22례정상인혈장표본,채용층차취류분석화주성분분석건립결책수모형,응용해모형맹사 21례폐부량성질병화47례Ⅰ기폐암.결과 사선도23개차이단백봉(P<0.001).맹사분석현시결책수모형진단민감성화특이성분별위72.34%화71.43%,양성예측치화음성예측치분별위85.0%화78.95%,진단정학성위72.06%.결론 응용SELDI-TOF-Ms기술초보건립적단백질모형위폐암적조기진단제공료신적기술평태.
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. Results Twenty-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.