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Non-targeted and targeted metabolomics approaches to diagnosing lung cancer and predicting patient prognosis

Lung cancer is the most common cause of cancer death in China. We characterized metabolic alterations in lung cancer using two analytical platforms: a non-targeted metabolic profiling strategy based on proton nuclear magnetic resonance ((1)H-NMR) spectroscopy and a targeted metabolic profiling strat...

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Autores principales: Zhang, Xiaoli, Zhu, Xinyue, Wang, Caihong, Zhang, Haixia, Cai, Zhiming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Impact Journals LLC 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5325375/
https://www.ncbi.nlm.nih.gov/pubmed/27566571
http://dx.doi.org/10.18632/oncotarget.11521
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author Zhang, Xiaoli
Zhu, Xinyue
Wang, Caihong
Zhang, Haixia
Cai, Zhiming
author_facet Zhang, Xiaoli
Zhu, Xinyue
Wang, Caihong
Zhang, Haixia
Cai, Zhiming
author_sort Zhang, Xiaoli
collection PubMed
description Lung cancer is the most common cause of cancer death in China. We characterized metabolic alterations in lung cancer using two analytical platforms: a non-targeted metabolic profiling strategy based on proton nuclear magnetic resonance ((1)H-NMR) spectroscopy and a targeted metabolic profiling strategy based on rapid resolution liquid chromatography (RRLC). Changes in serum metabolite levels during oncogenesis were evaluated in 25 stage I lung cancer patients and matched healthy controls. We identified 25 metabolites that were differentially regulated between the lung cancer patients and matched controls. Of those, 16 were detected using the non-targeted approach and 9 were identified using the targeted approach. Both groups of metabolites could differentiate between lung cancer patients and healthy controls with 100% sensitivity and specificity. The principal metabolic alternations in lung cancer included changes in glycolysis, lipid metabolism, choline phospholipid metabolism, one-carbon metabolism, and amino acid metabolism. The targeted metabolomics approach was more sensitive, accurate, and specific than the non-targeted metabolomics approach. However, our data suggest that both metabolomics strategies could be used to detect early-stage lung cancer and predict patient prognosis.
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spelling pubmed-53253752017-03-23 Non-targeted and targeted metabolomics approaches to diagnosing lung cancer and predicting patient prognosis Zhang, Xiaoli Zhu, Xinyue Wang, Caihong Zhang, Haixia Cai, Zhiming Oncotarget Research Paper Lung cancer is the most common cause of cancer death in China. We characterized metabolic alterations in lung cancer using two analytical platforms: a non-targeted metabolic profiling strategy based on proton nuclear magnetic resonance ((1)H-NMR) spectroscopy and a targeted metabolic profiling strategy based on rapid resolution liquid chromatography (RRLC). Changes in serum metabolite levels during oncogenesis were evaluated in 25 stage I lung cancer patients and matched healthy controls. We identified 25 metabolites that were differentially regulated between the lung cancer patients and matched controls. Of those, 16 were detected using the non-targeted approach and 9 were identified using the targeted approach. Both groups of metabolites could differentiate between lung cancer patients and healthy controls with 100% sensitivity and specificity. The principal metabolic alternations in lung cancer included changes in glycolysis, lipid metabolism, choline phospholipid metabolism, one-carbon metabolism, and amino acid metabolism. The targeted metabolomics approach was more sensitive, accurate, and specific than the non-targeted metabolomics approach. However, our data suggest that both metabolomics strategies could be used to detect early-stage lung cancer and predict patient prognosis. Impact Journals LLC 2016-08-23 /pmc/articles/PMC5325375/ /pubmed/27566571 http://dx.doi.org/10.18632/oncotarget.11521 Text en Copyright: © 2016 Zhang et al. http://creativecommons.org/licenses/by/2.5/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Paper
Zhang, Xiaoli
Zhu, Xinyue
Wang, Caihong
Zhang, Haixia
Cai, Zhiming
Non-targeted and targeted metabolomics approaches to diagnosing lung cancer and predicting patient prognosis
title Non-targeted and targeted metabolomics approaches to diagnosing lung cancer and predicting patient prognosis
title_full Non-targeted and targeted metabolomics approaches to diagnosing lung cancer and predicting patient prognosis
title_fullStr Non-targeted and targeted metabolomics approaches to diagnosing lung cancer and predicting patient prognosis
title_full_unstemmed Non-targeted and targeted metabolomics approaches to diagnosing lung cancer and predicting patient prognosis
title_short Non-targeted and targeted metabolomics approaches to diagnosing lung cancer and predicting patient prognosis
title_sort non-targeted and targeted metabolomics approaches to diagnosing lung cancer and predicting patient prognosis
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5325375/
https://www.ncbi.nlm.nih.gov/pubmed/27566571
http://dx.doi.org/10.18632/oncotarget.11521
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