GC-MS-based untargeted metabolic profiling of malignant mesothelioma plasma

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. By using gas chromatography-mass spectrometr...

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Bibliographic Details
Published in:PeerJ (San Francisco, CA) CA), 2023-05, Vol.11, p.e15302, Article e15302
Main Authors: Wang, Ding, Zhu, Jing, Li, Na, Lu, Hongyang, Gao, Yun, Zhuang, Lei, Chen, Zhongjian, Mao, Weimin
Format: Article
Language:English
Subjects:
Alanine
Asbestos
Aspartate
Biochemistry
Bioinformatics
Biomarker
Gas Chromatography-Mass Spectrometry
GC-MS
Global Health
Health aspects
Humans
Malignant mesothelioma
Mass spectrometry
Mesothelioma
Mesothelioma, Malignant
Metabolic Sciences
Metabolites
Metabolomics
Monosaccharides
Oncology
Resveratrol
Sugars
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Summary: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. 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. Using samples from MM ( = 19) and healthy control ( = 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 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. 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.
ISSN:2167-8359
2167-8359
DOI:10.7717/PEERJ.15302