郭冰清,孙运,褚美娟,武隆丰,蒋学慧,汪曣,穆新林
单位
1.天津大学精密仪器与光电子工程学院；2.天津大学天津市生物医学检测技术与仪器重点实验室；3.北京大学人民医院
中文摘要:利用质子转移反应质谱(PTR-MS)对 40 名肺癌患者、32 名健康志愿者呼出气体中的挥发性有机物(VOCs)进行检测,Mann-Whitney秩和检验与多因素logistic回归的结果表明，呼气中VOC 33、VOC 39、VOC 45可能为肺癌人群的呼气标志物，同时VOC 45在小细胞肺癌和非小细胞肺癌患者之间存在显著差异。以肺癌人群的呼气标志物作为自变量，采用二元logistic回归分析和Fisher判别分析分别建立肺癌预测模型。logistic回归模型的受试者工作曲线下面积(AUC)达到 0.878，灵敏度和特异性分别为 85.5% 和 63.5%。Fisher 判别模型的受试者工作曲线下面积(AUC)达到 0.822，灵敏度和特异性分别为 82.5%和 62.5%。两种模型对肺癌的预测均具有统计学意义。
Dectection of Characteristic VOCs in Exhaled Breath of Lung Cancer Patients by Proton Transfer Reaction Mass Spectrometry
Abstract:In this paper,a method was developed for the detection of characteristic VOCs in exhaled breath of lung cancer patients by proton transfer reaction mass spectrometry(PTR-MS).An improved breath analysis system was used for 32 normal volunteers and 40 lung cancer patients.The data were statistically analyzed by Mann-Whitney test and logistic regression.The results showed that analytes VOC 33，VOC 39 and VOC 45 might be the breath markers for lung cancer patients,while VOC 45 was significantly different between patients with small cell lung cancer and non-small cell lung cancer.The area derived from the receiver operating curve(AUC),under the logistic regression model,reached to 0.878,with the sensitivity and specificity of 85.5% and 63.5%,respectively,while through the Fisher discriminant model,the area from AUC reached to 0.822,with sensitivity and specificity of 82.5% and 62.5%,respectively.The two models were both statistically significant for lung cancer prediction.
Key Words:lung cancer patients breath biomarkers PTR-MS VOCs non-invasive analysis
引用本文：郭冰清,孙运,褚美娟,武隆丰,蒋学慧,汪曣,穆新林.质子转移反应质谱对肺癌患者呼气中特征性VOCs的筛选及研究[J].分析测试学报,2018,37(3):263-268.
