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Metabolomics of a cell line-derived xenograft model reveals circulating metabolic signatures for malignant mesothelioma
BACKGROUND: Malignant mesothelioma (MM) is a rare and highly aggressive cancer. Despite advances in multidisciplinary treatments for cancer, the prognosis for MM remains poor with no effective diagnostic biomarkers currently available. The aim of this study was to identify plasma metabolic biomarker...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8740518/ https://www.ncbi.nlm.nih.gov/pubmed/35036082 http://dx.doi.org/10.7717/peerj.12568 |
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author | Gao, Yun Dai, Ziyi Yang, Chenxi Wang, Ding Guo, Zhenying Mao, Weimin Chen, Zhongjian |
author_facet | Gao, Yun Dai, Ziyi Yang, Chenxi Wang, Ding Guo, Zhenying Mao, Weimin Chen, Zhongjian |
author_sort | Gao, Yun |
collection | PubMed |
description | BACKGROUND: Malignant mesothelioma (MM) is a rare and highly aggressive cancer. Despite advances in multidisciplinary treatments for cancer, the prognosis for MM remains poor with no effective diagnostic biomarkers currently available. The aim of this study was to identify plasma metabolic biomarkers for better MM diagnosis and prognosis by use of a MM cell line-derived xenograft (CDX) model. METHODS: The MM CDX model was confirmed by hematoxylin and eosin staining and immunohistochemistry. Twenty female nude mice were randomly divided into two groups, 10 for the MM CDX model and 10 controls. Plasma samples were collected two weeks after tumor cell implantation. Gas chromatography-mass spectrometry analysis was conducted. Both univariate and multivariate statistics were used to select potential metabolic biomarkers. Hierarchical clustering analysis, metabolic pathway analysis, and receiver operating characteristic (ROC) analysis were performed. Additionally, bioinformatics analysis was used to investigate differential genes between tumor and normal tissues, and survival-associated genes. RESULTS: The MM CDX model was successfully established. With VIP > 1.0 and P-value < 0.05, a total of 23 differential metabolites were annotated, in which isoleucine, 5-dihydrocortisol, and indole-3-acetamide had the highest diagnostic values based on ROC analysis. These were mainly enriched in pathways for starch and sucrose metabolism, pentose and glucuronate interconversions, galactose metabolism, steroid hormone biosynthesis, as well as phenylalanine, tyrosine and tryptophan biosynthesis. Further, down-regulation was observed for amino acids, especially isoleucine, which is consistent with up-regulation of amino acid transporter genes SLC7A5 and SLC1A3 in MM. Overall survival was also negatively associated with SLC1A5, SLC7A5, and SLC1A3. CONCLUSION: We found several altered plasma metabolites in the MM CDX model. The importance of specific metabolic pathways, for example amino acid metabolism, is herein highlighted, although further investigation is warranted. |
format | Online Article Text |
id | pubmed-8740518 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-87405182022-01-14 Metabolomics of a cell line-derived xenograft model reveals circulating metabolic signatures for malignant mesothelioma Gao, Yun Dai, Ziyi Yang, Chenxi Wang, Ding Guo, Zhenying Mao, Weimin Chen, Zhongjian PeerJ Biochemistry BACKGROUND: Malignant mesothelioma (MM) is a rare and highly aggressive cancer. Despite advances in multidisciplinary treatments for cancer, the prognosis for MM remains poor with no effective diagnostic biomarkers currently available. The aim of this study was to identify plasma metabolic biomarkers for better MM diagnosis and prognosis by use of a MM cell line-derived xenograft (CDX) model. METHODS: The MM CDX model was confirmed by hematoxylin and eosin staining and immunohistochemistry. Twenty female nude mice were randomly divided into two groups, 10 for the MM CDX model and 10 controls. Plasma samples were collected two weeks after tumor cell implantation. Gas chromatography-mass spectrometry analysis was conducted. Both univariate and multivariate statistics were used to select potential metabolic biomarkers. Hierarchical clustering analysis, metabolic pathway analysis, and receiver operating characteristic (ROC) analysis were performed. Additionally, bioinformatics analysis was used to investigate differential genes between tumor and normal tissues, and survival-associated genes. RESULTS: The MM CDX model was successfully established. With VIP > 1.0 and P-value < 0.05, a total of 23 differential metabolites were annotated, in which isoleucine, 5-dihydrocortisol, and indole-3-acetamide had the highest diagnostic values based on ROC analysis. These were mainly enriched in pathways for starch and sucrose metabolism, pentose and glucuronate interconversions, galactose metabolism, steroid hormone biosynthesis, as well as phenylalanine, tyrosine and tryptophan biosynthesis. Further, down-regulation was observed for amino acids, especially isoleucine, which is consistent with up-regulation of amino acid transporter genes SLC7A5 and SLC1A3 in MM. Overall survival was also negatively associated with SLC1A5, SLC7A5, and SLC1A3. CONCLUSION: We found several altered plasma metabolites in the MM CDX model. The importance of specific metabolic pathways, for example amino acid metabolism, is herein highlighted, although further investigation is warranted. PeerJ Inc. 2022-01-04 /pmc/articles/PMC8740518/ /pubmed/35036082 http://dx.doi.org/10.7717/peerj.12568 Text en ©2022 Gao 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 Gao, Yun Dai, Ziyi Yang, Chenxi Wang, Ding Guo, Zhenying Mao, Weimin Chen, Zhongjian Metabolomics of a cell line-derived xenograft model reveals circulating metabolic signatures for malignant mesothelioma |
title | Metabolomics of a cell line-derived xenograft model reveals circulating metabolic signatures for malignant mesothelioma |
title_full | Metabolomics of a cell line-derived xenograft model reveals circulating metabolic signatures for malignant mesothelioma |
title_fullStr | Metabolomics of a cell line-derived xenograft model reveals circulating metabolic signatures for malignant mesothelioma |
title_full_unstemmed | Metabolomics of a cell line-derived xenograft model reveals circulating metabolic signatures for malignant mesothelioma |
title_short | Metabolomics of a cell line-derived xenograft model reveals circulating metabolic signatures for malignant mesothelioma |
title_sort | metabolomics of a cell line-derived xenograft model reveals circulating metabolic signatures for malignant mesothelioma |
topic | Biochemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8740518/ https://www.ncbi.nlm.nih.gov/pubmed/35036082 http://dx.doi.org/10.7717/peerj.12568 |
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