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Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma
Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high perfo...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7366718/ https://www.ncbi.nlm.nih.gov/pubmed/32678093 http://dx.doi.org/10.1038/s41467-020-17347-6 |
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author | Huang, Lin Wang, Lin Hu, Xiaomeng Chen, Sen Tao, Yunwen Su, Haiyang Yang, Jing Xu, Wei Vedarethinam, Vadanasundari Wu, Shu Liu, Bin Wan, Xinze Lou, Jiatao Wang, Qian Qian, Kun |
author_facet | Huang, Lin Wang, Lin Hu, Xiaomeng Chen, Sen Tao, Yunwen Su, Haiyang Yang, Jing Xu, Wei Vedarethinam, Vadanasundari Wu, Shu Liu, Bin Wan, Xinze Lou, Jiatao Wang, Qian Qian, Kun |
author_sort | Huang, Lin |
collection | PubMed |
description | Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 s using only 50 nL of serum. We define a metabolic range of 100–400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70–90% and specificity~90–93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls (p < 0.05). Our approach advances the design of metabolic analysis for early cancer detection and holds promise as an efficient test for low-cost rollout to clinics. |
format | Online Article Text |
id | pubmed-7366718 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-73667182020-07-21 Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma Huang, Lin Wang, Lin Hu, Xiaomeng Chen, Sen Tao, Yunwen Su, Haiyang Yang, Jing Xu, Wei Vedarethinam, Vadanasundari Wu, Shu Liu, Bin Wan, Xinze Lou, Jiatao Wang, Qian Qian, Kun Nat Commun Article Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 s using only 50 nL of serum. We define a metabolic range of 100–400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70–90% and specificity~90–93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls (p < 0.05). Our approach advances the design of metabolic analysis for early cancer detection and holds promise as an efficient test for low-cost rollout to clinics. Nature Publishing Group UK 2020-07-16 /pmc/articles/PMC7366718/ /pubmed/32678093 http://dx.doi.org/10.1038/s41467-020-17347-6 Text en © The Author(s) 2020 Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Huang, Lin Wang, Lin Hu, Xiaomeng Chen, Sen Tao, Yunwen Su, Haiyang Yang, Jing Xu, Wei Vedarethinam, Vadanasundari Wu, Shu Liu, Bin Wan, Xinze Lou, Jiatao Wang, Qian Qian, Kun Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma |
title | Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma |
title_full | Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma |
title_fullStr | Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma |
title_full_unstemmed | Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma |
title_short | Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma |
title_sort | machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7366718/ https://www.ncbi.nlm.nih.gov/pubmed/32678093 http://dx.doi.org/10.1038/s41467-020-17347-6 |
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