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Urinary Volatomic Expression Pattern: Paving the Way for Identification of Potential Candidate Biosignatures for Lung Cancer
The urinary volatomic profiling of Indian cohorts composed of 28 lung cancer (LC) patients and 27 healthy subjects (control group, CTRL) was established using headspace solid phase microextraction technique combined with gas chromatography mass spectrometry methodology as a powerful approach to iden...
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Published in: | Metabolites 2022-01, Vol.12 (1), p.36 |
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description | The urinary volatomic profiling of Indian cohorts composed of 28 lung cancer (LC) patients and 27 healthy subjects (control group, CTRL) was established using headspace solid phase microextraction technique combined with gas chromatography mass spectrometry methodology as a powerful approach to identify urinary volatile organic metabolites (uVOMs) to discriminate among LC patients from CTRL. Overall, 147 VOMs of several chemistries were identified in the intervention groups-including naphthalene derivatives, phenols, and organosulphurs-augmented in the LC group. In contrast, benzene and terpenic derivatives were found to be more prevalent in the CTRL group. The volatomic data obtained were processed using advanced statistical analysis, namely partial least square discriminative analysis (PLS-DA), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) methods. This resulted in the identification of nine uVOMs with a higher potential to discriminate LC patients from CTRL subjects. These were furan, o-cymene, furfural, linalool oxide, viridiflorene, 2-bromo-phenol, tricyclazole, 4-methyl-phenol, and 1-(4-hydroxy-3,5-di-tert-butylphenyl)-2-methyl-3-morpholinopropan-1-one. The metabolic pathway analysis of the data obtained identified several altered biochemical pathways in LC mainly affecting glycolysis/gluconeogenesis, pyruvate metabolism, and fatty acid biosynthesis. Moreover, acetate and octanoic, decanoic, and dodecanoic fatty acids were identified as the key metabolites responsible for such deregulation. Furthermore, studies involving larger cohorts of LC patients would allow us to consolidate the data obtained and challenge the potential of the uVOMs as candidate biomarkers for LC. |
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Overall, 147 VOMs of several chemistries were identified in the intervention groups-including naphthalene derivatives, phenols, and organosulphurs-augmented in the LC group. In contrast, benzene and terpenic derivatives were found to be more prevalent in the CTRL group. The volatomic data obtained were processed using advanced statistical analysis, namely partial least square discriminative analysis (PLS-DA), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) methods. This resulted in the identification of nine uVOMs with a higher potential to discriminate LC patients from CTRL subjects. These were furan, o-cymene, furfural, linalool oxide, viridiflorene, 2-bromo-phenol, tricyclazole, 4-methyl-phenol, and 1-(4-hydroxy-3,5-di-tert-butylphenyl)-2-methyl-3-morpholinopropan-1-one. The metabolic pathway analysis of the data obtained identified several altered biochemical pathways in LC mainly affecting glycolysis/gluconeogenesis, pyruvate metabolism, and fatty acid biosynthesis. Moreover, acetate and octanoic, decanoic, and dodecanoic fatty acids were identified as the key metabolites responsible for such deregulation. Furthermore, studies involving larger cohorts of LC patients would allow us to consolidate the data obtained and challenge the potential of the uVOMs as candidate biomarkers for LC.</description><identifier>ISSN: 2218-1989</identifier><identifier>EISSN: 2218-1989</identifier><identifier>DOI: 10.3390/metabo12010036</identifier><identifier>PMID: 35050157</identifier><language>eng</language><publisher>Switzerland: MDPI AG</publisher><subject>Accuracy ; Acetic acid ; Benzene ; Biomarkers ; Cymene ; Discriminant analysis ; Disease ; Fatty acids ; Furfural ; Gas chromatography ; GC-qMS ; Gluconeogenesis ; Glycolysis ; Headspace ; HS-SPME ; Hydrocarbons ; Linalool ; Lung cancer ; lung cancer (LC) biomarkers ; Mass spectroscopy ; Metabolic pathways ; Metabolism ; Metabolites ; Naphthalene ; Phenols ; Pyruvic acid ; Solid phase methods ; Statistical analysis ; Tomography ; Trends ; Urine ; Variables ; Viridiflorene ; volatile organic metabolites (VOMs)</subject><ispartof>Metabolites, 2022-01, Vol.12 (1), p.36</ispartof><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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Overall, 147 VOMs of several chemistries were identified in the intervention groups-including naphthalene derivatives, phenols, and organosulphurs-augmented in the LC group. In contrast, benzene and terpenic derivatives were found to be more prevalent in the CTRL group. The volatomic data obtained were processed using advanced statistical analysis, namely partial least square discriminative analysis (PLS-DA), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) methods. This resulted in the identification of nine uVOMs with a higher potential to discriminate LC patients from CTRL subjects. These were furan, o-cymene, furfural, linalool oxide, viridiflorene, 2-bromo-phenol, tricyclazole, 4-methyl-phenol, and 1-(4-hydroxy-3,5-di-tert-butylphenyl)-2-methyl-3-morpholinopropan-1-one. The metabolic pathway analysis of the data obtained identified several altered biochemical pathways in LC mainly affecting glycolysis/gluconeogenesis, pyruvate metabolism, and fatty acid biosynthesis. Moreover, acetate and octanoic, decanoic, and dodecanoic fatty acids were identified as the key metabolites responsible for such deregulation. Furthermore, studies involving larger cohorts of LC patients would allow us to consolidate the data obtained and challenge the potential of the uVOMs as candidate biomarkers for LC.</description><subject>Accuracy</subject><subject>Acetic acid</subject><subject>Benzene</subject><subject>Biomarkers</subject><subject>Cymene</subject><subject>Discriminant analysis</subject><subject>Disease</subject><subject>Fatty acids</subject><subject>Furfural</subject><subject>Gas chromatography</subject><subject>GC-qMS</subject><subject>Gluconeogenesis</subject><subject>Glycolysis</subject><subject>Headspace</subject><subject>HS-SPME</subject><subject>Hydrocarbons</subject><subject>Linalool</subject><subject>Lung cancer</subject><subject>lung cancer (LC) biomarkers</subject><subject>Mass spectroscopy</subject><subject>Metabolic pathways</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>Naphthalene</subject><subject>Phenols</subject><subject>Pyruvic acid</subject><subject>Solid phase methods</subject><subject>Statistical analysis</subject><subject>Tomography</subject><subject>Trends</subject><subject>Urine</subject><subject>Variables</subject><subject>Viridiflorene</subject><subject>volatile organic metabolites (VOMs)</subject><issn>2218-1989</issn><issn>2218-1989</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdkk1vEzEQhi0EolXolSNaiQuXFH-svV4OSDQqbaRI9EDhaM16Z1NHm3WwvRWV-PF4k1I1-OLR-J1H846HkLeMngtR049bTNB4ximjVKgX5JRzpues1vXLZ_EJOYtxQ_NRVFaUvSYnQlJJmaxOyZ_b4AYID8UP30PyW2eLy9-7gDE6PxQ3kBKG4VMO7t2wLtIdFj_hoeh8KJYtDsl1zkKapL4rbnyaUtAXCxha10LC4sL56NYDpDEz93WrMYOywGJ4Q1510Ec8e7xn5Pbr5ffF9Xz17Wq5-LKa21KXaQ4aUQKyutGWaUWrSmDXSa60zv460ViFtq0BpJJWgsCStp0QSjSMl0xoMSPLA7f1sDG74LbZsfHgzD7hw9pASM72aATlWEEtmaR1abGsoW2k5FpYzZRVPLM-H1i7sdlia7PjAP0R9PhlcHdm7e-NrjQVcgJ8eAQE_2vEmMzWRYt9DwP6MRquOFOKT53PyPv_pBs_hiGPaq8Sgon88TNyflDZ4GMM2D01w6iZ9sQc70kuePfcwpP831aIv2NtuoA</recordid><startdate>20220104</startdate><enddate>20220104</enddate><creator>Taunk, Khushman</creator><creator>Porto-Figueira, Priscilla</creator><creator>Pereira, Jorge A M</creator><creator>Taware, Ravindra</creator><creator>da Costa, Nattane Luíza</creator><creator>Barbosa, Rommel</creator><creator>Rapole, Srikanth</creator><creator>Câmara, José S</creator><general>MDPI AG</general><general>MDPI</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QR</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M7P</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-0273-0819</orcidid><orcidid>https://orcid.org/0000-0003-0316-5348</orcidid><orcidid>https://orcid.org/0000-0003-2293-2936</orcidid><orcidid>https://orcid.org/0000-0003-1965-3151</orcidid></search><sort><creationdate>20220104</creationdate><title>Urinary Volatomic Expression Pattern: Paving the Way for Identification of Potential Candidate Biosignatures for Lung Cancer</title><author>Taunk, Khushman ; 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Overall, 147 VOMs of several chemistries were identified in the intervention groups-including naphthalene derivatives, phenols, and organosulphurs-augmented in the LC group. In contrast, benzene and terpenic derivatives were found to be more prevalent in the CTRL group. The volatomic data obtained were processed using advanced statistical analysis, namely partial least square discriminative analysis (PLS-DA), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) methods. This resulted in the identification of nine uVOMs with a higher potential to discriminate LC patients from CTRL subjects. These were furan, o-cymene, furfural, linalool oxide, viridiflorene, 2-bromo-phenol, tricyclazole, 4-methyl-phenol, and 1-(4-hydroxy-3,5-di-tert-butylphenyl)-2-methyl-3-morpholinopropan-1-one. 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subjects | Accuracy Acetic acid Benzene Biomarkers Cymene Discriminant analysis Disease Fatty acids Furfural Gas chromatography GC-qMS Gluconeogenesis Glycolysis Headspace HS-SPME Hydrocarbons Linalool Lung cancer lung cancer (LC) biomarkers Mass spectroscopy Metabolic pathways Metabolism Metabolites Naphthalene Phenols Pyruvic acid Solid phase methods Statistical analysis Tomography Trends Urine Variables Viridiflorene volatile organic metabolites (VOMs) |
title | Urinary Volatomic Expression Pattern: Paving the Way for Identification of Potential Candidate Biosignatures for Lung Cancer |
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