Volatolomics analysis of exhaled breath and gastric-endoluminal gas for distinguishing early upper gastrointestinal cancer from benign.

IF 3.7 4区 医学 Q1 BIOCHEMICAL RESEARCH METHODS
Chengfang Xiang, Hang Yang, Zhongjun Zhao, Fulong Deng, Yantong Lv, Yanting Yang, Yixiang Duan, Wenwen Li, Bing Hu
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引用次数: 2

Abstract

Exhaled breath and gastric-endoluminal gas (volatile products of diseased tissues) contain a large number of volatile organic compounds, which are valuable for early diagnosis of upper gastrointestinal (UGI) cancer. In this study, exhaled breath and gastric-endoluminal gas of patients with UGI cancer and benign disease were analyzed by gas chromatography-mass spectrometry (GC-MS) and ultraviolet photoionization time-of-flight mass spectrometry (UVP-TOFMS) to construct UGI cancer diagnostic models. Breath samples of 116 UGI cancer and 77 benign disease subjects and gastric-endoluminal gas samples of 114 UGI cancer and 76 benign disease subjects were collected. Machine learning (ML) algorithms were used to construct UGI cancer diagnostic models. Classification models based on exhaled breath for distinguishing UGI cancer from the benign group have area under the curve (AUC) of receiver operating characteristic curve values of 0.959 and 0.994 corresponding to GC-MS and UVP-TOFMS analysis, respectively. The AUC values of models based on gastric-endoluminal gas for UGI cancer and benign group classification are 0.935 and 0.929 corresponding to GC-MS and UVP-TOFMS analysis, respectively. This work indicates that volatolomics analysis of exhaled breath and gastric-endoluminal diseased tissues have great potential in early screening of UGI cancer. Moreover, gastric-endoluminal gas can be a means of gas biopsy to provide auxiliary information for the examination of tissue lesions during gastroscopy.

呼气和胃腔内气体挥发组学分析鉴别早期上消化道肿瘤与良性肿瘤。
呼出的气体和胃腔内气体(病变组织的挥发性产物)含有大量挥发性有机化合物,对上胃肠道(UGI)癌症的早期诊断有价值。本研究采用气相色谱-质谱联用(GC-MS)和紫外光电离飞行时间质谱联用(UVP-TOFMS)对UGI癌及良性疾病患者的呼出气体和胃腔内气体进行分析,建立UGI癌诊断模型。收集了116例UGI癌和77例良性疾病受试者的呼气样本,114例UGI癌和76例良性疾病受试者的胃内气体样本。采用机器学习(ML)算法构建UGI癌症诊断模型。呼气分类模型鉴别UGI癌与良性组的GC-MS和UVP-TOFMS分析对应的受试者工作特征曲线下面积(AUC)分别为0.959和0.994。GC-MS和UVP-TOFMS分析的胃腔内气体模型UGI癌组和良性组的AUC值分别为0.935和0.929。这项工作表明,呼气和胃-腔内病变组织的挥发组学分析在早期筛查UGI癌症方面具有很大的潜力。此外,胃腔内气体可以作为气体活检的一种手段,为胃镜检查过程中组织病变的检查提供辅助信息。
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来源期刊
Journal of breath research
Journal of breath research BIOCHEMICAL RESEARCH METHODS-RESPIRATORY SYSTEM
CiteScore
7.60
自引率
21.10%
发文量
49
审稿时长
>12 weeks
期刊介绍: Journal of Breath Research is dedicated to all aspects of scientific breath research. The traditional focus is on analysis of volatile compounds and aerosols in exhaled breath for the investigation of exogenous exposures, metabolism, toxicology, health status and the diagnosis of disease and breath odours. The journal also welcomes other breath-related topics. Typical areas of interest include: Big laboratory instrumentation: describing new state-of-the-art analytical instrumentation capable of performing high-resolution discovery and targeted breath research; exploiting complex technologies drawn from other areas of biochemistry and genetics for breath research. Engineering solutions: developing new breath sampling technologies for condensate and aerosols, for chemical and optical sensors, for extraction and sample preparation methods, for automation and standardization, and for multiplex analyses to preserve the breath matrix and facilitating analytical throughput. Measure exhaled constituents (e.g. CO2, acetone, isoprene) as markers of human presence or mitigate such contaminants in enclosed environments. Human and animal in vivo studies: decoding the ''breath exposome'', implementing exposure and intervention studies, performing cross-sectional and case-control research, assaying immune and inflammatory response, and testing mammalian host response to infections and exogenous exposures to develop information directly applicable to systems biology. Studying inhalation toxicology; inhaled breath as a source of internal dose; resultant blood, breath and urinary biomarkers linked to inhalation pathway. Cellular and molecular level in vitro studies. Clinical, pharmacological and forensic applications. Mathematical, statistical and graphical data interpretation.
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