| Title: | Target Analysis of Volatile Organic Compounds in Exhaled Breath for Lung Cancer Discrimination from Other Pulmonary Diseases and Healthy Persons |
| Author(s): | Koureas M; Kirgou P; Amoutzias G; Hadjichristodoulou C; Gourgoulianis K; Tsakalof A; |
| Address: | "Department of Hygiene and Epidemiology, University Hospital of Larissa, Faculty of Medicine, University of Thessaly, 22 Papakyriazi Street, 41222 Larissa, Greece. Respiratory Medicine Department, University Hospital of Larissa, Faculty of Medicine, University of Thessaly, 41500 Larissa, Greece. Bioinformatics Laboratory, Department of Biochemistry and Biotechnology, University of Thessaly, 41500 Larissa, Greece. Department of Biochemistry, Faculty of Medicine, University of Thessaly, 41500 Larissa, Greece" |
| ISSN/ISBN: | 2218-1989 (Print) 2218-1989 (Electronic) 2218-1989 (Linking) |
| Abstract: | "The aim of the present study was to investigate the ability of breath analysis to distinguish lung cancer (LC) patients from patients with other respiratory diseases and healthy people. The population sample consisted of 51 patients with confirmed LC, 38 patients with pathological computed tomography (CT) findings not diagnosed with LC, and 53 healthy controls. The concentrations of 19 volatile organic compounds (VOCs) were quantified in the exhaled breath of study participants by solid phase microextraction (SPME) of the VOCs and subsequent gas chromatography-mass spectrometry (GC-MS) analysis. Kruskal-Wallis and Mann-Whitney tests were used to identify significant differences between subgroups. Machine learning methods were used to determine the discriminant power of the method. Several compounds were found to differ significantly between LC patients and healthy controls. Strong associations were identified for 2-propanol, 1-propanol, toluene, ethylbenzene, and styrene (p-values < 0.001-0.006). These associations remained significant when ambient air concentrations were subtracted from breath concentrations. VOC levels were found to be affected by ambient air concentrations and a few by smoking status. The random forest machine learning algorithm achieved a correct classification of patients of 88.5% (area under the curve-AUC 0.94). However, none of the methods used achieved adequate discrimination between LC patients and patients with abnormal computed tomography (CT) findings. Biomarker sets, consisting mainly of the exogenous monoaromatic compounds and 1- and 2- propanol, adequately discriminated LC patients from healthy controls. The breath concentrations of these compounds may reflect the alterations in patient's physiological and biochemical status and perhaps can be used as probes for the investigation of these statuses or normalization of patient-related factors in breath analysis" |
| Keywords: | exhaled breath lung cancer machine learning volatile organic compounds; |
| Notes: | "PubMed-not-MEDLINEKoureas, Michalis Kirgou, Paraskevi Amoutzias, Grigoris Hadjichristodoulou, Christos Gourgoulianis, Konstantinos Tsakalof, Andreas eng 5394.02.07/Stavros Niarchos Foundation/ Switzerland 2020/08/07 Metabolites. 2020 Aug 3; 10(8):317. doi: 10.3390/metabo10080317" |
