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J Mass Spectrom

Title:		Exhaled breath analysis using on-line preconcentration mass spectrometry for gastric cancer diagnosis
Author(s):		Hong Y; Che X; Su H; Mai Z; Huang Z; Huang W; Chen W; Liu S; Gao W; Zhou Z; Tan G; Li X;
Address:		"Institute of Environmental Pollution and Health, School of Environmental and Chemical Engineering, Shanghai University, Shanghai, China. Guangzhou Hexin Instrument Co., LTD., Guangzhou, China. Institute of Mass Spectrometry and Atmospheric Environment, Jinan University, Guangzhou, China. Guangdong Provincial Engineering Research Center for On-line Source Apportionment System of Air Pollution, Guangzhou, China. Department of Gastrointestinal Surgery, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. Institute of High Energy Physics, the Chinese Academy of Sciences, Beijing, China"
Journal Title:		J Mass Spectrom
Year:		2021
Volume:		20200707
Issue:		4
Page Number:		e4588 -
DOI:		10.1002/jms.4588
ISSN/ISBN:		1096-9888 (Electronic) 1076-5174 (Linking)
Abstract:		"Breath volatile biomarkers are capable of distinguishing patients with various cancers. However, high throughput analytical technology is a prerequisite to a large-cohort study intended to discover reliable breath biomarkers for cancer diagnosis. Single-photon ionization (SPI) is a universal ionization technology, and SPI-mass spectrometry (SPI-MS) shows a remarkable advantage in the comprehensive detection of volatile organic compounds (VOCs), in particular, nonpolar compounds. In this study, we have introduced SPI-MS coupled with on-line thermal desorption (TD-SPI-MS) to demonstrate nontarget analysis of breath VOCs for gastric cancer patients. The breath fingerprints of the gastric cancer patients were significantly distinct from that of the control group. Acetone, isoprene, 1,3-dioxolan-2-one, phenol, meta-xylene, 1,2,3-trimethylbenzene, and phenyl acetate showed higher relative peak intensities in the breath profiles of gastric cancer patients. A diagnostic prediction model was further developed by using a training set (121 samples) and validated with a test set (53 samples). The predication accuracy of the developed model was 96.2%, and the area under the curve (AUC) of the receiver operator characteristic curve (ROC) was 0.997, indicating a satisfactory prediction ability of the developed model. Thus, by taking gastric cancer as an example, we have shown that TD-SPI-MS will be a promising tool for high throughput analysis of breath samples to discover characteristic VOCs in patients with various cancers"
Keywords:		"Acetates/analysis Acetone/analysis Biomarkers, Tumor/*analysis Breath Tests/*methods Butadienes/analysis Cohort Studies Dioxolanes/analysis Exhalation Hemiterpenes/analysis Humans Mass Spectrometry/*methods Phenol/analysis Phenols/analysis ROC Curve Stoma;"
Notes:		"MedlineHong, Yi Che, Xinxin Su, Haibo Mai, Zebin Huang, Zhengxu Huang, Weibin Chen, Wei Liu, Shulin Gao, Wei Zhou, Zhen Tan, Guobin Li, Xue eng 2016TQ03R103/Special Support Plan for High-Level Talents in Guangdong Province/ 2016YFC0201002/National Key R&D Program of China/ 2017YFB1103900/National Key R&D Program of China/ England 2020/07/08 J Mass Spectrom. 2021 Apr; 56(4):e4588. doi: 10.1002/jms.4588. Epub 2020 Jul 7"