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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
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.
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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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