Suppr超能文献

分析呼气、组织和细胞系中肺癌的挥发性有机化合物生物标志物。

The analysis of volatile organic compounds biomarkers for lung cancer in exhaled breath, tissues and cell lines.

机构信息

Biosensor National Special Lab, Key Lab for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, Zhejiang University, Hangzhou, China.

出版信息

Cancer Biomark. 2012;11(4):129-37. doi: 10.3233/CBM-2012-00270.

Abstract

BACKGROUND

Volatile organic compounds (VOCs) biomarkers in breath provide a novel, noninvasive and quick approach to diagnosis lung cancer. The aim of the proposed study was to investigate the VOCs biomarkers in exhaled breath for lung cancer.

METHOD

The VOCs in exhaled breath of 88 lung cancer patients, 70 lung benign disease and 85 healthy people were analyzed by Solid Phase Micro Extraction - Gas Chromatography Mass Spectrometry (SPME-GCMS). Three types of lung cancer cells and 18 lung cancer patients' tissues were cultured in vitro. The VOCs in the headspace of these cultivations were analyzed as an evidence of production mechanism of the VOCs in breath. Three lung cancer diagnosis models were constructed respectively in exhaled breath samples using Linear Discriminant Analysis (LDA). Leave one out cross validation was employed to evaluate these models.

RESULTS

23 VOCs, whose areas under curve (AUC) of receiver operating characteristic (ROC) > 0.60 and p < 0.01, were confirmed as the VOCs biomarkers for lung cancer. Three diagnostic models based on 23 VOCs could easily discriminate lung cancer patients from controls with 96.47% sensitivity and 97.47% specificity. However, the discrimination between early stage and later stage lung cancer was not very obvious.

摘要

背景

呼出气中的挥发性有机化合物(VOCs)生物标志物为肺癌的诊断提供了一种新颖、非侵入性和快速的方法。本研究旨在探讨呼出气中 VOCs 生物标志物在肺癌诊断中的应用。

方法

采用固相微萃取-气相色谱质谱联用(SPME-GCMS)技术分析了 88 例肺癌患者、70 例肺部良性疾病患者和 85 例健康对照者呼出气中的 VOCs。体外培养三种肺癌细胞和 18 例肺癌患者的组织,分析这些培养物顶空的 VOCs,以证实呼吸中 VOCs 的产生机制。分别采用线性判别分析(LDA)构建了三个基于呼气样本的肺癌诊断模型。采用留一法交叉验证对这些模型进行评价。

结果

23 种 VOCs 的曲线下面积(AUC)大于 0.60,p < 0.01,被确认为肺癌的 VOCs 生物标志物。基于这 23 种 VOCs 的三个诊断模型能够非常敏感和特异地区分肺癌患者和对照组,其敏感性为 96.47%,特异性为 97.47%。然而,早期和晚期肺癌的区分并不十分明显。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验