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GC-MS-based untargeted metabolic profiling of malignant mesothelioma plasma
BACKGROUND: Malignant mesothelioma (MM) is a cancer caused mainly by asbestos exposure, and is aggressive and incurable. This study aimed to identify differential metabolites and metabolic pathways involved in the pathogenesis and diagnosis of malignant mesothelioma. METHODS: By using gas chromatogr...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10200095/ https://www.ncbi.nlm.nih.gov/pubmed/37220527 http://dx.doi.org/10.7717/peerj.15302 |
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author | Wang, Ding Zhu, Jing Li, Na Lu, Hongyang Gao, Yun Zhuang, Lei Chen, Zhongjian Mao, Weimin |
author_facet | Wang, Ding Zhu, Jing Li, Na Lu, Hongyang Gao, Yun Zhuang, Lei Chen, Zhongjian Mao, Weimin |
author_sort | Wang, Ding |
collection | PubMed |
description | BACKGROUND: Malignant mesothelioma (MM) is a cancer caused mainly by asbestos exposure, and is aggressive and incurable. This study aimed to identify differential metabolites and metabolic pathways involved in the pathogenesis and diagnosis of malignant mesothelioma. METHODS: By using gas chromatography-mass spectrometry (GC-MS), this study examined the plasma metabolic profile of human malignant mesothelioma. We performed univariate and multivariate analyses and pathway analyses to identify differential metabolites, enriched metabolism pathways, and potential metabolic targets. The area under the receiver-operating curve (AUC) criterion was used to identify possible plasma biomarkers. RESULTS: Using samples from MM (n = 19) and healthy control (n = 22) participants, 20 metabolites were annotated. Seven metabolic pathways were disrupted, involving alanine, aspartate, and glutamate metabolism; glyoxylate and dicarboxylate metabolism; arginine and proline metabolism; butanoate and histidine metabolism; beta-alanine metabolism; and pentose phosphate metabolic pathway. The AUC was used to identify potential plasma biomarkers. Using a threshold of AUC = 0.9, five metabolites were identified, including xanthurenic acid, (s)-3,4-hydroxybutyric acid, D-arabinose, gluconic acid, and beta-d-glucopyranuronic acid. CONCLUSIONS: To the best of our knowledge, this is the first report of a plasma metabolomics analysis using GC-MS analyses of Asian MM patients. Our identification of these metabolic abnormalities is critical for identifying plasma biomarkers in patients with MM. However, additional research using a larger population is needed to validate our findings. |
format | Online Article Text |
id | pubmed-10200095 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102000952023-05-22 GC-MS-based untargeted metabolic profiling of malignant mesothelioma plasma Wang, Ding Zhu, Jing Li, Na Lu, Hongyang Gao, Yun Zhuang, Lei Chen, Zhongjian Mao, Weimin PeerJ Biochemistry BACKGROUND: Malignant mesothelioma (MM) is a cancer caused mainly by asbestos exposure, and is aggressive and incurable. This study aimed to identify differential metabolites and metabolic pathways involved in the pathogenesis and diagnosis of malignant mesothelioma. METHODS: By using gas chromatography-mass spectrometry (GC-MS), this study examined the plasma metabolic profile of human malignant mesothelioma. We performed univariate and multivariate analyses and pathway analyses to identify differential metabolites, enriched metabolism pathways, and potential metabolic targets. The area under the receiver-operating curve (AUC) criterion was used to identify possible plasma biomarkers. RESULTS: Using samples from MM (n = 19) and healthy control (n = 22) participants, 20 metabolites were annotated. Seven metabolic pathways were disrupted, involving alanine, aspartate, and glutamate metabolism; glyoxylate and dicarboxylate metabolism; arginine and proline metabolism; butanoate and histidine metabolism; beta-alanine metabolism; and pentose phosphate metabolic pathway. The AUC was used to identify potential plasma biomarkers. Using a threshold of AUC = 0.9, five metabolites were identified, including xanthurenic acid, (s)-3,4-hydroxybutyric acid, D-arabinose, gluconic acid, and beta-d-glucopyranuronic acid. CONCLUSIONS: To the best of our knowledge, this is the first report of a plasma metabolomics analysis using GC-MS analyses of Asian MM patients. Our identification of these metabolic abnormalities is critical for identifying plasma biomarkers in patients with MM. However, additional research using a larger population is needed to validate our findings. PeerJ Inc. 2023-05-18 /pmc/articles/PMC10200095/ /pubmed/37220527 http://dx.doi.org/10.7717/peerj.15302 Text en © 2023 Wang et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Biochemistry Wang, Ding Zhu, Jing Li, Na Lu, Hongyang Gao, Yun Zhuang, Lei Chen, Zhongjian Mao, Weimin GC-MS-based untargeted metabolic profiling of malignant mesothelioma plasma |
title | GC-MS-based untargeted metabolic profiling of malignant mesothelioma plasma |
title_full | GC-MS-based untargeted metabolic profiling of malignant mesothelioma plasma |
title_fullStr | GC-MS-based untargeted metabolic profiling of malignant mesothelioma plasma |
title_full_unstemmed | GC-MS-based untargeted metabolic profiling of malignant mesothelioma plasma |
title_short | GC-MS-based untargeted metabolic profiling of malignant mesothelioma plasma |
title_sort | gc-ms-based untargeted metabolic profiling of malignant mesothelioma plasma |
topic | Biochemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10200095/ https://www.ncbi.nlm.nih.gov/pubmed/37220527 http://dx.doi.org/10.7717/peerj.15302 |
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