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Circulating metabolites as potential biomarkers for the early detection and prognosis surveillance of gastrointestinal cancers
BACKGROUND AND AIMS: Two of the most lethal gastrointestinal (GI) cancers, gastric cancer (GC) and colon cancer (CC), are ranked in the top five cancers that cause deaths worldwide. Most GI cancer deaths can be reduced by earlier detection and more appropriate medical treatment. Unlike the current “...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10073066/ https://www.ncbi.nlm.nih.gov/pubmed/37014438 http://dx.doi.org/10.1007/s11306-023-02002-0 |
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author | Song, Guodong Wang, Li Tang, Junlong Li, Haohui Pang, Shuyu Li, Yan Liu, Li Hu, Junyuan |
author_facet | Song, Guodong Wang, Li Tang, Junlong Li, Haohui Pang, Shuyu Li, Yan Liu, Li Hu, Junyuan |
author_sort | Song, Guodong |
collection | PubMed |
description | BACKGROUND AND AIMS: Two of the most lethal gastrointestinal (GI) cancers, gastric cancer (GC) and colon cancer (CC), are ranked in the top five cancers that cause deaths worldwide. Most GI cancer deaths can be reduced by earlier detection and more appropriate medical treatment. Unlike the current “gold standard” techniques, non-invasive and highly sensitive screening tests are required for GI cancer diagnosis. Here, we explored the potential of metabolomics for GI cancer detection and the classification of tissue-of-origin, and even the prognosis management. METHODS: Plasma samples from 37 gastric cancer (GC), 17 colon cancer (CC), and 27 non-cancer (NC) patients were prepared for metabolomics and lipidomics analysis by three MS-based platforms. Univariate, multivariate, and clustering analyses were used for selecting significant metabolic features. ROC curve analysis was based on a series of different binary classifications as well as the true-positive rate (sensitivity) and the false-positive rate (1-specificity). RESULTS: GI cancers exhibited obvious metabolic perturbation compared with benign diseases. The differentiated metabolites of gastric cancer (GC) and colon cancer (CC) were targeted to same pathways but with different degrees of cellular metabolism reprogramming. The cancer-specific metabolites distinguished the malignant and benign, and classified the cancer types. We also applied this test to before- and after-surgery samples, wherein surgical resection significantly altered the blood-metabolic patterns. There were 15 metabolites significantly altered in GC and CC patients who underwent surgical treatment, and partly returned to normal conditions. CONCLUSION: Blood-based metabolomics analysis is an efficient strategy for GI cancer screening, especially for malignant and benign diagnoses. The cancer-specific metabolic patterns process the potential for classifying tissue-of-origin in multi-cancer screening. Besides, the circulating metabolites for prognosis management of GI cancer is a promising area of research. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11306-023-02002-0. |
format | Online Article Text |
id | pubmed-10073066 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-100730662023-04-06 Circulating metabolites as potential biomarkers for the early detection and prognosis surveillance of gastrointestinal cancers Song, Guodong Wang, Li Tang, Junlong Li, Haohui Pang, Shuyu Li, Yan Liu, Li Hu, Junyuan Metabolomics Original Article BACKGROUND AND AIMS: Two of the most lethal gastrointestinal (GI) cancers, gastric cancer (GC) and colon cancer (CC), are ranked in the top five cancers that cause deaths worldwide. Most GI cancer deaths can be reduced by earlier detection and more appropriate medical treatment. Unlike the current “gold standard” techniques, non-invasive and highly sensitive screening tests are required for GI cancer diagnosis. Here, we explored the potential of metabolomics for GI cancer detection and the classification of tissue-of-origin, and even the prognosis management. METHODS: Plasma samples from 37 gastric cancer (GC), 17 colon cancer (CC), and 27 non-cancer (NC) patients were prepared for metabolomics and lipidomics analysis by three MS-based platforms. Univariate, multivariate, and clustering analyses were used for selecting significant metabolic features. ROC curve analysis was based on a series of different binary classifications as well as the true-positive rate (sensitivity) and the false-positive rate (1-specificity). RESULTS: GI cancers exhibited obvious metabolic perturbation compared with benign diseases. The differentiated metabolites of gastric cancer (GC) and colon cancer (CC) were targeted to same pathways but with different degrees of cellular metabolism reprogramming. The cancer-specific metabolites distinguished the malignant and benign, and classified the cancer types. We also applied this test to before- and after-surgery samples, wherein surgical resection significantly altered the blood-metabolic patterns. There were 15 metabolites significantly altered in GC and CC patients who underwent surgical treatment, and partly returned to normal conditions. CONCLUSION: Blood-based metabolomics analysis is an efficient strategy for GI cancer screening, especially for malignant and benign diagnoses. The cancer-specific metabolic patterns process the potential for classifying tissue-of-origin in multi-cancer screening. Besides, the circulating metabolites for prognosis management of GI cancer is a promising area of research. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11306-023-02002-0. Springer US 2023-04-04 2023 /pmc/articles/PMC10073066/ /pubmed/37014438 http://dx.doi.org/10.1007/s11306-023-02002-0 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Song, Guodong Wang, Li Tang, Junlong Li, Haohui Pang, Shuyu Li, Yan Liu, Li Hu, Junyuan Circulating metabolites as potential biomarkers for the early detection and prognosis surveillance of gastrointestinal cancers |
title | Circulating metabolites as potential biomarkers for the early detection and prognosis surveillance of gastrointestinal cancers |
title_full | Circulating metabolites as potential biomarkers for the early detection and prognosis surveillance of gastrointestinal cancers |
title_fullStr | Circulating metabolites as potential biomarkers for the early detection and prognosis surveillance of gastrointestinal cancers |
title_full_unstemmed | Circulating metabolites as potential biomarkers for the early detection and prognosis surveillance of gastrointestinal cancers |
title_short | Circulating metabolites as potential biomarkers for the early detection and prognosis surveillance of gastrointestinal cancers |
title_sort | circulating metabolites as potential biomarkers for the early detection and prognosis surveillance of gastrointestinal cancers |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10073066/ https://www.ncbi.nlm.nih.gov/pubmed/37014438 http://dx.doi.org/10.1007/s11306-023-02002-0 |
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