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Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts

Despite recent progress in our understanding of the association between the gut microbiome and colorectal cancer (CRC), multi-kingdom gut microbiome dysbiosis in CRC across cohorts is unexplored. We investigated four-kingdom microbiota alterations using CRC metagenomic datasets of 1,368 samples from...

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Autores principales: Liu, Ning-Ning, Jiao, Na, Tan, Jing-Cong, Wang, Ziliang, Wu, Dingfeng, Wang, An-Jun, Chen, Jie, Tao, Liwen, Zhou, Chenfen, Fang, Wenjie, Cheong, Io Hong, Pan, Weihua, Liao, Wanqing, Kozlakidis, Zisis, Heeschen, Christopher, Moore, Geromy G., Zhu, Lixin, Chen, Xingdong, Zhang, Guoqing, Zhu, Ruixin, Wang, Hui
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8813618/
https://www.ncbi.nlm.nih.gov/pubmed/35087227
http://dx.doi.org/10.1038/s41564-021-01030-7
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author Liu, Ning-Ning
Jiao, Na
Tan, Jing-Cong
Wang, Ziliang
Wu, Dingfeng
Wang, An-Jun
Chen, Jie
Tao, Liwen
Zhou, Chenfen
Fang, Wenjie
Cheong, Io Hong
Pan, Weihua
Liao, Wanqing
Kozlakidis, Zisis
Heeschen, Christopher
Moore, Geromy G.
Zhu, Lixin
Chen, Xingdong
Zhang, Guoqing
Zhu, Ruixin
Wang, Hui
author_facet Liu, Ning-Ning
Jiao, Na
Tan, Jing-Cong
Wang, Ziliang
Wu, Dingfeng
Wang, An-Jun
Chen, Jie
Tao, Liwen
Zhou, Chenfen
Fang, Wenjie
Cheong, Io Hong
Pan, Weihua
Liao, Wanqing
Kozlakidis, Zisis
Heeschen, Christopher
Moore, Geromy G.
Zhu, Lixin
Chen, Xingdong
Zhang, Guoqing
Zhu, Ruixin
Wang, Hui
author_sort Liu, Ning-Ning
collection PubMed
description Despite recent progress in our understanding of the association between the gut microbiome and colorectal cancer (CRC), multi-kingdom gut microbiome dysbiosis in CRC across cohorts is unexplored. We investigated four-kingdom microbiota alterations using CRC metagenomic datasets of 1,368 samples from 8 distinct geographical cohorts. Integrated analysis identified 20 archaeal, 27 bacterial, 20 fungal and 21 viral species for each single-kingdom diagnostic model. However, our data revealed superior diagnostic accuracy for models constructed with multi-kingdom markers, in particular the addition of fungal species. Specifically, 16 multi-kingdom markers including 11 bacterial, 4 fungal and 1 archaeal feature, achieved good performance in diagnosing patients with CRC (area under the receiver operating characteristic curve (AUROC) = 0.83) and maintained accuracy across 3 independent cohorts. Coabundance analysis of the ecological network revealed associations between bacterial and fungal species, such as Talaromyces islandicus and Clostridium saccharobutylicum. Using metagenome shotgun sequencing data, the predictive power of the microbial functional potential was explored and elevated D-amino acid metabolism and butanoate metabolism were observed in CRC. Interestingly, the diagnostic model based on functional EggNOG genes achieved high accuracy (AUROC = 0.86). Collectively, our findings uncovered CRC-associated microbiota common across cohorts and demonstrate the applicability of multi-kingdom and functional markers as CRC diagnostic tools and, potentially, as therapeutic targets for the treatment of CRC.
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spelling pubmed-88136182022-02-09 Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts Liu, Ning-Ning Jiao, Na Tan, Jing-Cong Wang, Ziliang Wu, Dingfeng Wang, An-Jun Chen, Jie Tao, Liwen Zhou, Chenfen Fang, Wenjie Cheong, Io Hong Pan, Weihua Liao, Wanqing Kozlakidis, Zisis Heeschen, Christopher Moore, Geromy G. Zhu, Lixin Chen, Xingdong Zhang, Guoqing Zhu, Ruixin Wang, Hui Nat Microbiol Article Despite recent progress in our understanding of the association between the gut microbiome and colorectal cancer (CRC), multi-kingdom gut microbiome dysbiosis in CRC across cohorts is unexplored. We investigated four-kingdom microbiota alterations using CRC metagenomic datasets of 1,368 samples from 8 distinct geographical cohorts. Integrated analysis identified 20 archaeal, 27 bacterial, 20 fungal and 21 viral species for each single-kingdom diagnostic model. However, our data revealed superior diagnostic accuracy for models constructed with multi-kingdom markers, in particular the addition of fungal species. Specifically, 16 multi-kingdom markers including 11 bacterial, 4 fungal and 1 archaeal feature, achieved good performance in diagnosing patients with CRC (area under the receiver operating characteristic curve (AUROC) = 0.83) and maintained accuracy across 3 independent cohorts. Coabundance analysis of the ecological network revealed associations between bacterial and fungal species, such as Talaromyces islandicus and Clostridium saccharobutylicum. Using metagenome shotgun sequencing data, the predictive power of the microbial functional potential was explored and elevated D-amino acid metabolism and butanoate metabolism were observed in CRC. Interestingly, the diagnostic model based on functional EggNOG genes achieved high accuracy (AUROC = 0.86). Collectively, our findings uncovered CRC-associated microbiota common across cohorts and demonstrate the applicability of multi-kingdom and functional markers as CRC diagnostic tools and, potentially, as therapeutic targets for the treatment of CRC. Nature Publishing Group UK 2022-01-27 2022 /pmc/articles/PMC8813618/ /pubmed/35087227 http://dx.doi.org/10.1038/s41564-021-01030-7 Text en © The Author(s) 2022 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
Liu, Ning-Ning
Jiao, Na
Tan, Jing-Cong
Wang, Ziliang
Wu, Dingfeng
Wang, An-Jun
Chen, Jie
Tao, Liwen
Zhou, Chenfen
Fang, Wenjie
Cheong, Io Hong
Pan, Weihua
Liao, Wanqing
Kozlakidis, Zisis
Heeschen, Christopher
Moore, Geromy G.
Zhu, Lixin
Chen, Xingdong
Zhang, Guoqing
Zhu, Ruixin
Wang, Hui
Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts
title Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts
title_full Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts
title_fullStr Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts
title_full_unstemmed Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts
title_short Multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts
title_sort multi-kingdom microbiota analyses identify bacterial–fungal interactions and biomarkers of colorectal cancer across cohorts
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8813618/
https://www.ncbi.nlm.nih.gov/pubmed/35087227
http://dx.doi.org/10.1038/s41564-021-01030-7
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