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浙江大学学报(工学版)  2019, Vol. 53 Issue (12): 2389-2395    DOI: 10.3785/j.issn.1008-973X.2019.12.017
计算机科学与人工智能     
肺癌呼吸标志物筛选及其生物信息学分析
吴谦(),王平*()
浙江大学 生物医学工程教育部重点实验室,浙江 杭州 310027
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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摘要:

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    
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.

Key words: exhale breath detection    lung cancer biomarker    bioinformatics    transcriptome analysis    protein structure analysis    early screening of lung cancer
收稿日期: 2018-10-03 出版日期: 2019-12-17
CLC:  R 318  
基金资助: 国家自然科学基金重大仪器专项资助项目(31627801)
通讯作者: 王平     E-mail: qianwu@zju.edu.cn;cnpwang@zju.edu.cn
作者简介: 吴谦(1989—),男,博士生,从事生物医学工程的研究. orcid.org/0000-0002-0739-364X. E-mail: qianwu@zju.edu.cn
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引用本文:

吴谦,王平. 肺癌呼吸标志物筛选及其生物信息学分析[J]. 浙江大学学报(工学版), 2019, 53(12): 2389-2395.

Qian WU,Ping WANG. Screening and bioinformatics analysis of lung cancer exhale breath biomarkers. Journal of ZheJiang University (Engineering Science), 2019, 53(12): 2389-2395.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2019.12.017        http://www.zjujournals.com/eng/CN/Y2019/V53/I12/2389

临床信息 肺癌实验组人数 健康对照组人数
性别 39 76
19 49
吸烟状况 吸烟 22 47
戒烟 8 18
非吸烟 28 60
肺癌类型 腺癌 29 ?
鳞癌 26 ?
大细胞癌 0 ?
小细胞癌 3 ?
非小细胞癌分期 I 10 ?
II 6 ?
III 11 ?
IV 28 ?
小细胞癌分期 局限期 0 ?
广泛期 3 ?
表 1  受试者基本临床信息统计
图 1  受试者呼吸气体的采集和分析
金标准 测试结果
阴性 阳性
注:假阳性率=B/(A+B),特异度=1?假阳性率;真阳性率=D/(C+D),灵敏度=真阳性率
阴性 真阴性(A) 假阳性(B)
阳性 假阴性(C) 真阳性(D)
表 2  二分类逻辑回归计算方法
VOCs AUCs P 95%置信区间
下限 上限
3-乙基甲苯 0.882 <0.001 0.827 0.936
1,2,3-三甲苯 0.876 <0.001 0.825 0.928
丙基苯 0.842 <0.001 0.783 0.902
正丙基环己烷 0.840 <0.001 0.780 0.900
茚满 0.801 <0.001 0.737 0.865
1-甲基-3-丙基苯 0.800 <0.001 0.734 0.865
邻二甲苯 0.773 <0.001 0.707 0.839
4-甲基-2-戊酮 0.763 <0.001 0.687 0.839
正己醛 0.758 <0.001 0.686 0.830
甲基环己烷 0.753 <0.001 0.681 0.825
表 3  肺癌特异性挥发性有机物(VOC)的分析结果
图 2  由edgeR和DESeq分析得到共有差异表达基因的韦恩图
VOCs 类别 代谢过程 关键酶
正丙基环己烷
甲基环己烷
烷烃类 氧化应激反应 细胞色素p450
4-甲基-2-戊酮
正己醛
醛酮类 脂质过氧化反应
脱氢作用
细胞色素p450
醇脱氢酶
3-乙基甲苯
1,2,3-三甲苯
丙基苯
茚满
1-甲基-3-丙基苯
邻二甲苯
烷基苯 自身免疫防御 细胞色素p450
谷胱甘肽S转移酶
磺基转移酶
乙酰转移酶
表 4  人体内产生VOC的代谢通路和关键酶
图 3  醛脱氢酶1A3的蛋白结构
图 4  突变后的醛脱氢酶1A3在411位赖氨酸与NAD和413位苯丙氨酸发生空间碰撞
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