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Breath Analysis for Early Detection of Malignant Pleural Mesothelioma: Volatile Organic Compounds (VOCs) Determination and Possible Biochemical Pathways

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Department of Biology, University of Bari Aldo Moro, 70126 Bari, Italy
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Apulian Breath Analysis Center (CeRBA), IRCCS Giovanni Paolo II, 70124 Bari, Italy
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Thoracic Oncology Unit, IRCCS, Istituto Tumori Giovanni Paolo II, 70124 Bari, Italy
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Section of Bari, National Institute for Nuclear Physics, 70126 Bari, Italy
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Department of Physics, University of Bari Aldo Moro, 70126 Bari, Italy
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Department of Soil, Plant and Food Science, University of Bari Aldo Moro, 70126 Bari, Italy
*
Authors to whom correspondence should be addressed.
Gianluigi de Gennaro, Domenico Galetta and Sabina Tangaro are co-last authors.
Cancers 2020, 12(5), 1262; https://doi.org/10.3390/cancers12051262
Received: 27 March 2020 / Revised: 17 April 2020 / Accepted: 8 May 2020 / Published: 16 May 2020
Malignant pleural mesothelioma (MPM) is a rare neoplasm, mainly caused by asbestos exposure, with a high mortality rate. The management of patients with MPM is controversial due to a long latency period between exposure and diagnosis and because of non-specific symptoms generally appearing at advanced stage of the disease. Breath analysis, aimed at the identification of diagnostic Volatile Organic Compounds (VOCs) pattern in exhaled breath, is believed to improve early detection of MPM. Therefore, in this study, breath samples from 14 MPM patients and 20 healthy controls (HC) were collected and analyzed by Thermal Desorption-Gas Chromatography-Mass Spectrometry (TD-GC/MS). Nonparametric test allowed to identify the most weighting variables to discriminate between MPM and HC breath samples and multivariate statistics were applied. Considering that MPM is an aggressive neoplasm leading to a late diagnosis and thus the recruitment of patients is very difficult, a promising data mining approach was developed and validated in order to discriminate between MPM patients and healthy controls, even if no large population data are available. Three different machine learning algorithms were applied to perform the classification task with a leave-one-out cross-validation approach, leading to remarkable results (Area Under Curve AUC = 93%). Ten VOCs, such as ketones, alkanes and methylate derivates, as well as hydrocarbons, were able to discriminate between MPM patients and healthy controls and for each compound which resulted diagnostic for MPM, the metabolic pathway was studied in order to identify the link between VOC and the neoplasm. Moreover, five breath samples from asymptomatic asbestos-exposed persons (AEx) were exploratively analyzed, processed and tested by the validated statistical method as blinded samples in order to evaluate the performance for the early recognition of patients affected by MPM among asbestos-exposed persons. Good agreement was found between the information obtained by gold-standard diagnostic methods such as computed tomography CT and model output. View Full-Text
Keywords: breath analysis; Malignant Pleural Mesothelioma (MPM); Volatile Organic Compounds (VOCs); TD-GC/MS; machine learning; metabolic pathways breath analysis; Malignant Pleural Mesothelioma (MPM); Volatile Organic Compounds (VOCs); TD-GC/MS; machine learning; metabolic pathways
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Di Gilio, A.; Catino, A.; Lombardi, A.; Palmisani, J.; Facchini, L.; Mongelli, T.; Varesano, N.; Bellotti, R.; Galetta, D.; de Gennaro, G.; Tangaro, S. Breath Analysis for Early Detection of Malignant Pleural Mesothelioma: Volatile Organic Compounds (VOCs) Determination and Possible Biochemical Pathways. Cancers 2020, 12, 1262.

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