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Aging characteristics of colorectal cancer based on gut microbiota

BACKGROUND: Aging is one of the factors leading to cancer. Gut microbiota is related to aging and colorectal cancer (CRC). METHODS: A total of 11 metagenomic data sets related to CRC were collected from the R package curated Metagenomic Data. After batch effect correction, healthy individuals and CR...

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Autores principales: Wu, Yinhang, Zhuang, Jing, Zhang, Qi, Zhao, Xingming, Chen, Gong, Han, Shugao, Hu, Boyang, Wu, Wei, Han, Shuwen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10524056/
https://www.ncbi.nlm.nih.gov/pubmed/37548332
http://dx.doi.org/10.1002/cam4.6414
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author Wu, Yinhang
Zhuang, Jing
Zhang, Qi
Zhao, Xingming
Chen, Gong
Han, Shugao
Hu, Boyang
Wu, Wei
Han, Shuwen
author_facet Wu, Yinhang
Zhuang, Jing
Zhang, Qi
Zhao, Xingming
Chen, Gong
Han, Shugao
Hu, Boyang
Wu, Wei
Han, Shuwen
author_sort Wu, Yinhang
collection PubMed
description BACKGROUND: Aging is one of the factors leading to cancer. Gut microbiota is related to aging and colorectal cancer (CRC). METHODS: A total of 11 metagenomic data sets related to CRC were collected from the R package curated Metagenomic Data. After batch effect correction, healthy individuals and CRC samples were divided into three age groups. Ggplot2 and Microbiota Process packages were used for visual description of species composition and PCA in healthy individuals and CRC samples. LEfSe analysis was performed for species relative abundance data in healthy/CRC groups according to age. Spearman correlation coefficient of age‐differentiated bacteria in healthy individuals and CRC samples was calculated separately. Finally, the age prediction model and CRC risk prediction model were constructed based on the age‐differentiated bacteria. RESULTS: The structure and composition of the gut microbiota were significantly different among the three groups. For example, the abundance of Bacteroides vulgatus in the old group was lower than that in the other two groups, the abundance of Bacteroides fragilis increased with aging. In addition, seven species of bacteria whose abundance increases with aging were screened out. Furthermore, the abundance of pathogenic bacteria (Escherichia_coli, Butyricimonas_virosa, Ruminococcus_bicirculans, Bacteroides_fragilis and Streptococcus_vestibularis) increased with aging in CRCs. The abundance of probiotics (Eubacterium_eligens) decreased with aging in CRCs. The age prediction model for healthy individuals based on the 80 age‐related differential bacteria and model of CRC patients based on the 58 age‐related differential bacteria performed well, with AUC of 0.79 and 0.71, respectively. The AUC of CRC risk prediction model based on 45 disease differential bacteria was 0.83. After removing the intersection between the disease‐differentiated bacteria and the age‐differentiated bacteria from the healthy samples, the AUC of CRC risk prediction model based on remaining 31 bacteria was 0.8. CRC risk prediction models for each of the three age groups showed no significant difference in accuracy (young: AUC=0.82, middle: AUC=0.83, old: AUC=0.85). CONCLUSION: Age as a factor affecting microbial composition should be considered in the application of gut microbiota to predict the risk of CRC.
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spelling pubmed-105240562023-09-28 Aging characteristics of colorectal cancer based on gut microbiota Wu, Yinhang Zhuang, Jing Zhang, Qi Zhao, Xingming Chen, Gong Han, Shugao Hu, Boyang Wu, Wei Han, Shuwen Cancer Med RESEARCH ARTICLES BACKGROUND: Aging is one of the factors leading to cancer. Gut microbiota is related to aging and colorectal cancer (CRC). METHODS: A total of 11 metagenomic data sets related to CRC were collected from the R package curated Metagenomic Data. After batch effect correction, healthy individuals and CRC samples were divided into three age groups. Ggplot2 and Microbiota Process packages were used for visual description of species composition and PCA in healthy individuals and CRC samples. LEfSe analysis was performed for species relative abundance data in healthy/CRC groups according to age. Spearman correlation coefficient of age‐differentiated bacteria in healthy individuals and CRC samples was calculated separately. Finally, the age prediction model and CRC risk prediction model were constructed based on the age‐differentiated bacteria. RESULTS: The structure and composition of the gut microbiota were significantly different among the three groups. For example, the abundance of Bacteroides vulgatus in the old group was lower than that in the other two groups, the abundance of Bacteroides fragilis increased with aging. In addition, seven species of bacteria whose abundance increases with aging were screened out. Furthermore, the abundance of pathogenic bacteria (Escherichia_coli, Butyricimonas_virosa, Ruminococcus_bicirculans, Bacteroides_fragilis and Streptococcus_vestibularis) increased with aging in CRCs. The abundance of probiotics (Eubacterium_eligens) decreased with aging in CRCs. The age prediction model for healthy individuals based on the 80 age‐related differential bacteria and model of CRC patients based on the 58 age‐related differential bacteria performed well, with AUC of 0.79 and 0.71, respectively. The AUC of CRC risk prediction model based on 45 disease differential bacteria was 0.83. After removing the intersection between the disease‐differentiated bacteria and the age‐differentiated bacteria from the healthy samples, the AUC of CRC risk prediction model based on remaining 31 bacteria was 0.8. CRC risk prediction models for each of the three age groups showed no significant difference in accuracy (young: AUC=0.82, middle: AUC=0.83, old: AUC=0.85). CONCLUSION: Age as a factor affecting microbial composition should be considered in the application of gut microbiota to predict the risk of CRC. John Wiley and Sons Inc. 2023-08-07 /pmc/articles/PMC10524056/ /pubmed/37548332 http://dx.doi.org/10.1002/cam4.6414 Text en © 2023 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle RESEARCH ARTICLES
Wu, Yinhang
Zhuang, Jing
Zhang, Qi
Zhao, Xingming
Chen, Gong
Han, Shugao
Hu, Boyang
Wu, Wei
Han, Shuwen
Aging characteristics of colorectal cancer based on gut microbiota
title Aging characteristics of colorectal cancer based on gut microbiota
title_full Aging characteristics of colorectal cancer based on gut microbiota
title_fullStr Aging characteristics of colorectal cancer based on gut microbiota
title_full_unstemmed Aging characteristics of colorectal cancer based on gut microbiota
title_short Aging characteristics of colorectal cancer based on gut microbiota
title_sort aging characteristics of colorectal cancer based on gut microbiota
topic RESEARCH ARTICLES
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10524056/
https://www.ncbi.nlm.nih.gov/pubmed/37548332
http://dx.doi.org/10.1002/cam4.6414
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