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Evolutionary metabolic landscape from preneoplasia to invasive lung adenocarcinoma
Metabolic reprogramming evolves during cancer initiation and progression. However, thorough understanding of metabolic evolution from preneoplasia to lung adenocarcinoma (LUAD) is still limited. Here, we perform large-scale targeted metabolomics on resected lesions and plasma obtained from invasive...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8580984/ https://www.ncbi.nlm.nih.gov/pubmed/34759281 http://dx.doi.org/10.1038/s41467-021-26685-y |
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author | Nie, Meng Yao, Ke Zhu, Xinsheng Chen, Na Xiao, Nan Wang, Yi Peng, Bo Yao, LiAng Li, Peng Zhang, Peng Hu, Zeping |
author_facet | Nie, Meng Yao, Ke Zhu, Xinsheng Chen, Na Xiao, Nan Wang, Yi Peng, Bo Yao, LiAng Li, Peng Zhang, Peng Hu, Zeping |
author_sort | Nie, Meng |
collection | PubMed |
description | Metabolic reprogramming evolves during cancer initiation and progression. However, thorough understanding of metabolic evolution from preneoplasia to lung adenocarcinoma (LUAD) is still limited. Here, we perform large-scale targeted metabolomics on resected lesions and plasma obtained from invasive LUAD and its precursors, and decipher the metabolic trajectories from atypical adenomatous hyperplasia (AAH) to adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC), revealing that perturbed metabolic pathways emerge early in premalignant lesions. Furthermore, three panels of plasma metabolites are identified as non-invasive predictive biomarkers to distinguish IAC and its precursors with benign diseases. Strikingly, metabolomics clustering defines three metabolic subtypes of IAC patients with distinct clinical characteristics. We identify correlation between aberrant bile acid metabolism in subtype III with poor clinical features and demonstrate dysregulated bile acid metabolism promotes migration of LUAD, which could be exploited as potential targetable vulnerability and for stratifying patients. Collectively, the comprehensive landscape of the metabolic evolution along the development of LUAD will improve early detection and provide impactful therapeutic strategies. |
format | Online Article Text |
id | pubmed-8580984 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-85809842021-11-15 Evolutionary metabolic landscape from preneoplasia to invasive lung adenocarcinoma Nie, Meng Yao, Ke Zhu, Xinsheng Chen, Na Xiao, Nan Wang, Yi Peng, Bo Yao, LiAng Li, Peng Zhang, Peng Hu, Zeping Nat Commun Article Metabolic reprogramming evolves during cancer initiation and progression. However, thorough understanding of metabolic evolution from preneoplasia to lung adenocarcinoma (LUAD) is still limited. Here, we perform large-scale targeted metabolomics on resected lesions and plasma obtained from invasive LUAD and its precursors, and decipher the metabolic trajectories from atypical adenomatous hyperplasia (AAH) to adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IAC), revealing that perturbed metabolic pathways emerge early in premalignant lesions. Furthermore, three panels of plasma metabolites are identified as non-invasive predictive biomarkers to distinguish IAC and its precursors with benign diseases. Strikingly, metabolomics clustering defines three metabolic subtypes of IAC patients with distinct clinical characteristics. We identify correlation between aberrant bile acid metabolism in subtype III with poor clinical features and demonstrate dysregulated bile acid metabolism promotes migration of LUAD, which could be exploited as potential targetable vulnerability and for stratifying patients. Collectively, the comprehensive landscape of the metabolic evolution along the development of LUAD will improve early detection and provide impactful therapeutic strategies. Nature Publishing Group UK 2021-11-10 /pmc/articles/PMC8580984/ /pubmed/34759281 http://dx.doi.org/10.1038/s41467-021-26685-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Nie, Meng Yao, Ke Zhu, Xinsheng Chen, Na Xiao, Nan Wang, Yi Peng, Bo Yao, LiAng Li, Peng Zhang, Peng Hu, Zeping Evolutionary metabolic landscape from preneoplasia to invasive lung adenocarcinoma |
title | Evolutionary metabolic landscape from preneoplasia to invasive lung adenocarcinoma |
title_full | Evolutionary metabolic landscape from preneoplasia to invasive lung adenocarcinoma |
title_fullStr | Evolutionary metabolic landscape from preneoplasia to invasive lung adenocarcinoma |
title_full_unstemmed | Evolutionary metabolic landscape from preneoplasia to invasive lung adenocarcinoma |
title_short | Evolutionary metabolic landscape from preneoplasia to invasive lung adenocarcinoma |
title_sort | evolutionary metabolic landscape from preneoplasia to invasive lung adenocarcinoma |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8580984/ https://www.ncbi.nlm.nih.gov/pubmed/34759281 http://dx.doi.org/10.1038/s41467-021-26685-y |
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