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Integrative analysis of plasma metabolomics and proteomics reveals the metabolic landscape of breast cancer
BACKGROUND: Breast cancer (BC) is the most commonly diagnosed cancer. Currently, mammography and breast ultrasonography are the main clinical screening methods for BC. Our study aimed to reveal the specific metabolic profiles of BC patients and explore the specific metabolic signatures in human plas...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9382832/ https://www.ncbi.nlm.nih.gov/pubmed/35978348 http://dx.doi.org/10.1186/s40170-022-00289-6 |
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author | An, Rui Yu, Haitao Wang, Yanzhong Lu, Jie Gao, Yuzhen Xie, Xinyou Zhang, Jun |
author_facet | An, Rui Yu, Haitao Wang, Yanzhong Lu, Jie Gao, Yuzhen Xie, Xinyou Zhang, Jun |
author_sort | An, Rui |
collection | PubMed |
description | BACKGROUND: Breast cancer (BC) is the most commonly diagnosed cancer. Currently, mammography and breast ultrasonography are the main clinical screening methods for BC. Our study aimed to reveal the specific metabolic profiles of BC patients and explore the specific metabolic signatures in human plasma for BC diagnosis. METHODS: This study enrolled 216 participants, including BC patients, benign patients, and healthy controls (HC) and formed two cohorts, one training cohort and one testing cohort. Plasma samples were collected from each participant and subjected to perform nontargeted metabolomics and proteomics. The metabolic signatures for BC diagnosis were identified through machine learning. RESULTS: Metabolomics analysis revealed that BC patients showed a significant change of metabolic profiles compared to HC individuals. The alanine, aspartate and glutamate pathways, glutamine and glutamate metabolic pathways, and arginine biosynthesis pathways were the critical biological metabolic pathways in BC. Proteomics identified 29 upregulated and 2 downregulated proteins in BC. Our integrative analysis found that aspartate aminotransferase (GOT1), l-lactate dehydrogenase B chain (LDHB), glutathione synthetase (GSS), and glutathione peroxidase 3 (GPX3) were closely involved in these metabolic pathways. Support vector machine (SVM) demonstrated a predictive model with 47 metabolites, and this model achieved a high accuracy in BC prediction (AUC = 1). Besides, this panel of metabolites also showed a fairly high predictive power in the testing cohort between BC vs HC (AUC = 0.794), and benign vs HC (AUC = 0.879). CONCLUSIONS: This study uncovered specific changes in the metabolic and proteomic profiling of breast cancer patients and identified a panel of 47 plasma metabolites, including sphingomyelins, glutamate, and cysteine could be potential diagnostic biomarkers for breast cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40170-022-00289-6. |
format | Online Article Text |
id | pubmed-9382832 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-93828322022-08-18 Integrative analysis of plasma metabolomics and proteomics reveals the metabolic landscape of breast cancer An, Rui Yu, Haitao Wang, Yanzhong Lu, Jie Gao, Yuzhen Xie, Xinyou Zhang, Jun Cancer Metab Research BACKGROUND: Breast cancer (BC) is the most commonly diagnosed cancer. Currently, mammography and breast ultrasonography are the main clinical screening methods for BC. Our study aimed to reveal the specific metabolic profiles of BC patients and explore the specific metabolic signatures in human plasma for BC diagnosis. METHODS: This study enrolled 216 participants, including BC patients, benign patients, and healthy controls (HC) and formed two cohorts, one training cohort and one testing cohort. Plasma samples were collected from each participant and subjected to perform nontargeted metabolomics and proteomics. The metabolic signatures for BC diagnosis were identified through machine learning. RESULTS: Metabolomics analysis revealed that BC patients showed a significant change of metabolic profiles compared to HC individuals. The alanine, aspartate and glutamate pathways, glutamine and glutamate metabolic pathways, and arginine biosynthesis pathways were the critical biological metabolic pathways in BC. Proteomics identified 29 upregulated and 2 downregulated proteins in BC. Our integrative analysis found that aspartate aminotransferase (GOT1), l-lactate dehydrogenase B chain (LDHB), glutathione synthetase (GSS), and glutathione peroxidase 3 (GPX3) were closely involved in these metabolic pathways. Support vector machine (SVM) demonstrated a predictive model with 47 metabolites, and this model achieved a high accuracy in BC prediction (AUC = 1). Besides, this panel of metabolites also showed a fairly high predictive power in the testing cohort between BC vs HC (AUC = 0.794), and benign vs HC (AUC = 0.879). CONCLUSIONS: This study uncovered specific changes in the metabolic and proteomic profiling of breast cancer patients and identified a panel of 47 plasma metabolites, including sphingomyelins, glutamate, and cysteine could be potential diagnostic biomarkers for breast cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40170-022-00289-6. BioMed Central 2022-08-17 /pmc/articles/PMC9382832/ /pubmed/35978348 http://dx.doi.org/10.1186/s40170-022-00289-6 Text en © The Author(s) 2022 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research An, Rui Yu, Haitao Wang, Yanzhong Lu, Jie Gao, Yuzhen Xie, Xinyou Zhang, Jun Integrative analysis of plasma metabolomics and proteomics reveals the metabolic landscape of breast cancer |
title | Integrative analysis of plasma metabolomics and proteomics reveals the metabolic landscape of breast cancer |
title_full | Integrative analysis of plasma metabolomics and proteomics reveals the metabolic landscape of breast cancer |
title_fullStr | Integrative analysis of plasma metabolomics and proteomics reveals the metabolic landscape of breast cancer |
title_full_unstemmed | Integrative analysis of plasma metabolomics and proteomics reveals the metabolic landscape of breast cancer |
title_short | Integrative analysis of plasma metabolomics and proteomics reveals the metabolic landscape of breast cancer |
title_sort | integrative analysis of plasma metabolomics and proteomics reveals the metabolic landscape of breast cancer |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9382832/ https://www.ncbi.nlm.nih.gov/pubmed/35978348 http://dx.doi.org/10.1186/s40170-022-00289-6 |
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