| Computer Science and Artificial Intelligence |
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| Screening and bioinformatics analysis of lung cancer exhale breath biomarkers |
Qian WU( ),Ping WANG*() |
| Key Laboratory for Biomedical Engineering of Education Ministry, Zhejiang University, Hangzhou 310027, China |
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Abstract The exhale breath detection combined bioinformatics analysis method, including transcriptome, metabolic pathway and protein structure, was proposed to identify gas markers for screening and diagnosis of lung cancer. Lung cancer patients and healthy controls' samples were collected to performe GC-MS and ROC curve analysis which obtained ten specific VOCs. Differentially expressed genes were obtained by transcriptome analysis. The differentially expressed genes and relative metabolic pathways were consistent with in vivo biological process, which meant that these VOCs come from the metabolism of lung cancer patient. The sensitivity, specificity and overall accuracy of lung cancer diagnosis model established based on VOCs were 86.2%, 91.2% and 89.6%, respectively. Thus, the proposed method can distinguish normal people and lung cancer patients simply and effectively, providing convenient approach for early screening of lung cancer.
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Received: 03 October 2018
Published: 17 December 2019
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Corresponding Authors:
Ping WANG
E-mail: qianwu@zju.edu.cn;cnpwang@zju.edu.cn
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肺癌呼吸标志物筛选及其生物信息学分析
采用结合转录组、代谢通路、蛋白结构的呼出气体检测生物信息学分析方法来确定肺癌气体标志物，用于肺癌的筛选诊断. 采用标准仪器（GCMS）检测肺癌病人和正常人的呼吸气体样本；经统计分析，筛选出10种特异性挥发性有机物（VOC）. 采用转录组分析得到肺癌和健康人的差异表达基因，其富集的代谢通路与人体内产生VOC的代谢通路一致，证明所筛选的VOC标志物与肺癌病人代谢具有相关性. 基于此VOC建立的肺癌诊断模型的灵敏度、特异性和整体正确率分别为86.2%，91.2% 和89.6%，说明所提方法能简便、有效区分正常人和肺癌病人，为早期肺癌筛查提供方便、可靠的检测方法.
关键词：
呼出气体检测,
肺癌标志物,
生物信息学,
转录组分析,
蛋白结构分析,
肺癌早期筛查
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