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GC-MS Techniques Investigating Potential Biomarkers of Dying in the Last Weeks with Lung Cancer

Predicting when a patient with advanced cancer is dying is a challenge and currently no prognostic test is available. We hypothesised that a dying process from cancer is associated with metabolic changes and specifically with changes in volatile organic compounds (VOCs). We analysed urine from patie...

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Autores principales: Chapman, Elinor A., Baker, James, Aggarwal, Prashant, Hughes, David M., Nwosu, Amara C., Boyd, Mark T., Mayland, Catriona R., Mason, Stephen, Ellershaw, John, Probert, Chris S., Coyle, Séamus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9867309/
https://www.ncbi.nlm.nih.gov/pubmed/36675106
http://dx.doi.org/10.3390/ijms24021591
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author Chapman, Elinor A.
Baker, James
Aggarwal, Prashant
Hughes, David M.
Nwosu, Amara C.
Boyd, Mark T.
Mayland, Catriona R.
Mason, Stephen
Ellershaw, John
Probert, Chris S.
Coyle, Séamus
author_facet Chapman, Elinor A.
Baker, James
Aggarwal, Prashant
Hughes, David M.
Nwosu, Amara C.
Boyd, Mark T.
Mayland, Catriona R.
Mason, Stephen
Ellershaw, John
Probert, Chris S.
Coyle, Séamus
author_sort Chapman, Elinor A.
collection PubMed
description Predicting when a patient with advanced cancer is dying is a challenge and currently no prognostic test is available. We hypothesised that a dying process from cancer is associated with metabolic changes and specifically with changes in volatile organic compounds (VOCs). We analysed urine from patients with lung cancer in the last weeks of life by headspace gas chromatography mass spectrometry. Urine was acidified or alkalinised before analysis. VOC changes in the last weeks of life were identified using univariate, multivariate and linear regression analysis; 12 VOCs increased (11 from the acid dataset, 2 from the alkali dataset) and 25 VOCs decreased (23 from the acid dataset and 3 from the alkali dataset). A Cox Lasso prediction model using 8 VOCs predicted dying with an AUC of 0.77, 0.78 and 0.85 at 30, 20 and 10 days and stratified patients into a low (median 10 days), medium (median 50 days) or high risk of survival. Our data supports the hypothesis there are specific metabolic changes associated with the dying. The VOCs identified are potential biomarkers of dying in lung cancer and could be used as a tool to provide additional prognostic information to inform expert clinician judgement and subsequent decision making.
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spelling pubmed-98673092023-01-22 GC-MS Techniques Investigating Potential Biomarkers of Dying in the Last Weeks with Lung Cancer Chapman, Elinor A. Baker, James Aggarwal, Prashant Hughes, David M. Nwosu, Amara C. Boyd, Mark T. Mayland, Catriona R. Mason, Stephen Ellershaw, John Probert, Chris S. Coyle, Séamus Int J Mol Sci Article Predicting when a patient with advanced cancer is dying is a challenge and currently no prognostic test is available. We hypothesised that a dying process from cancer is associated with metabolic changes and specifically with changes in volatile organic compounds (VOCs). We analysed urine from patients with lung cancer in the last weeks of life by headspace gas chromatography mass spectrometry. Urine was acidified or alkalinised before analysis. VOC changes in the last weeks of life were identified using univariate, multivariate and linear regression analysis; 12 VOCs increased (11 from the acid dataset, 2 from the alkali dataset) and 25 VOCs decreased (23 from the acid dataset and 3 from the alkali dataset). A Cox Lasso prediction model using 8 VOCs predicted dying with an AUC of 0.77, 0.78 and 0.85 at 30, 20 and 10 days and stratified patients into a low (median 10 days), medium (median 50 days) or high risk of survival. Our data supports the hypothesis there are specific metabolic changes associated with the dying. The VOCs identified are potential biomarkers of dying in lung cancer and could be used as a tool to provide additional prognostic information to inform expert clinician judgement and subsequent decision making. MDPI 2023-01-13 /pmc/articles/PMC9867309/ /pubmed/36675106 http://dx.doi.org/10.3390/ijms24021591 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Article
Chapman, Elinor A.
Baker, James
Aggarwal, Prashant
Hughes, David M.
Nwosu, Amara C.
Boyd, Mark T.
Mayland, Catriona R.
Mason, Stephen
Ellershaw, John
Probert, Chris S.
Coyle, Séamus
GC-MS Techniques Investigating Potential Biomarkers of Dying in the Last Weeks with Lung Cancer
title GC-MS Techniques Investigating Potential Biomarkers of Dying in the Last Weeks with Lung Cancer
title_full GC-MS Techniques Investigating Potential Biomarkers of Dying in the Last Weeks with Lung Cancer
title_fullStr GC-MS Techniques Investigating Potential Biomarkers of Dying in the Last Weeks with Lung Cancer
title_full_unstemmed GC-MS Techniques Investigating Potential Biomarkers of Dying in the Last Weeks with Lung Cancer
title_short GC-MS Techniques Investigating Potential Biomarkers of Dying in the Last Weeks with Lung Cancer
title_sort gc-ms techniques investigating potential biomarkers of dying in the last weeks with lung cancer
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9867309/
https://www.ncbi.nlm.nih.gov/pubmed/36675106
http://dx.doi.org/10.3390/ijms24021591
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