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Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner
Breast cancer is the leading cause of female cancer-related deaths worldwide. New technologies with enhanced sensitivity and specificity for early diagnosis and monitoring of postoperative recurrence are in critical demand. Automatic breast full volume scanning system (ABVS) is an emerging technolog...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9597368/ https://www.ncbi.nlm.nih.gov/pubmed/36313729 http://dx.doi.org/10.3389/fonc.2022.939606 |
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author | Liu, Jianjun Zhou, Yang Liu, Huiying Ma, Mengyan Wang, Fei Liu, Chang Yuan, Qihang Wang, Hongjiang Hou, Xiukun Yin, Peiyuan |
author_facet | Liu, Jianjun Zhou, Yang Liu, Huiying Ma, Mengyan Wang, Fei Liu, Chang Yuan, Qihang Wang, Hongjiang Hou, Xiukun Yin, Peiyuan |
author_sort | Liu, Jianjun |
collection | PubMed |
description | Breast cancer is the leading cause of female cancer-related deaths worldwide. New technologies with enhanced sensitivity and specificity for early diagnosis and monitoring of postoperative recurrence are in critical demand. Automatic breast full volume scanning system (ABVS) is an emerging technology used as an alternative imaging method for breast cancer screening. Despite its improved detection rate of malignant tumors, ABVS cannot accurately stage breast cancer preoperatively in 30–40% of cases. As a major hallmark of breast cancer, the characteristic metabolic reprogramming may provide potential biomarkers as an auxiliary method for ABVS. OBJECTIVE: The objective of this study was to identify differential metabolomic signatures between benign and malignant breast tumors and among different subtypes of breast cancer patients based on untargeted metabolomics and improve breast cancer detection rate by combining key metabolites and ABVS. METHODS: Untargeted metabolomics approach was used to profile serum samples from 70 patients with different subtypes of breast cancer and benign breast tumor to determine specific metabolomic profiles through univariate and multivariate statistical data analysis. RESULTS: Metabolic profiles correctly distinguished benign and malignant breast tumors patients, and a total of 791 metabolites were identified. There were 54 different metabolites between benign and malignant breast tumors and 17 different metabolites between invasive and non-invasive breast cancer. Notably, the missed diagnosis rate of ABVS could be reduced by differential metabolite analysis. Moreover, the diagnostic performance analyses of combined metabolites (pelargonic acid, N-acetylasparagine, and cysteine-S-sulfate) with ABVS performance gave a ROC area under the curve of 0.967 (95% CI: 0.926, 0.993). CONCLUSIONS: Our study identified metabolic features both in benign and malignant breast tumors and in invasive and non-invasive breast cancer. Combined ultrasound ABVS and a panel of differential serum metabolites could further improve the accuracy of preoperative diagnosis of breast cancer and guide surgical therapy. |
format | Online Article Text |
id | pubmed-9597368 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95973682022-10-27 Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner Liu, Jianjun Zhou, Yang Liu, Huiying Ma, Mengyan Wang, Fei Liu, Chang Yuan, Qihang Wang, Hongjiang Hou, Xiukun Yin, Peiyuan Front Oncol Oncology Breast cancer is the leading cause of female cancer-related deaths worldwide. New technologies with enhanced sensitivity and specificity for early diagnosis and monitoring of postoperative recurrence are in critical demand. Automatic breast full volume scanning system (ABVS) is an emerging technology used as an alternative imaging method for breast cancer screening. Despite its improved detection rate of malignant tumors, ABVS cannot accurately stage breast cancer preoperatively in 30–40% of cases. As a major hallmark of breast cancer, the characteristic metabolic reprogramming may provide potential biomarkers as an auxiliary method for ABVS. OBJECTIVE: The objective of this study was to identify differential metabolomic signatures between benign and malignant breast tumors and among different subtypes of breast cancer patients based on untargeted metabolomics and improve breast cancer detection rate by combining key metabolites and ABVS. METHODS: Untargeted metabolomics approach was used to profile serum samples from 70 patients with different subtypes of breast cancer and benign breast tumor to determine specific metabolomic profiles through univariate and multivariate statistical data analysis. RESULTS: Metabolic profiles correctly distinguished benign and malignant breast tumors patients, and a total of 791 metabolites were identified. There were 54 different metabolites between benign and malignant breast tumors and 17 different metabolites between invasive and non-invasive breast cancer. Notably, the missed diagnosis rate of ABVS could be reduced by differential metabolite analysis. Moreover, the diagnostic performance analyses of combined metabolites (pelargonic acid, N-acetylasparagine, and cysteine-S-sulfate) with ABVS performance gave a ROC area under the curve of 0.967 (95% CI: 0.926, 0.993). CONCLUSIONS: Our study identified metabolic features both in benign and malignant breast tumors and in invasive and non-invasive breast cancer. Combined ultrasound ABVS and a panel of differential serum metabolites could further improve the accuracy of preoperative diagnosis of breast cancer and guide surgical therapy. Frontiers Media S.A. 2022-10-12 /pmc/articles/PMC9597368/ /pubmed/36313729 http://dx.doi.org/10.3389/fonc.2022.939606 Text en Copyright © 2022 Liu, Zhou, Liu, Ma, Wang, Liu, Yuan, Wang, Hou and Yin https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Liu, Jianjun Zhou, Yang Liu, Huiying Ma, Mengyan Wang, Fei Liu, Chang Yuan, Qihang Wang, Hongjiang Hou, Xiukun Yin, Peiyuan Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner |
title | Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner |
title_full | Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner |
title_fullStr | Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner |
title_full_unstemmed | Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner |
title_short | Metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner |
title_sort | metabolic reprogramming enables the auxiliary diagnosis of breast cancer by automated breast volume scanner |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9597368/ https://www.ncbi.nlm.nih.gov/pubmed/36313729 http://dx.doi.org/10.3389/fonc.2022.939606 |
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