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Identification of intestinal microbiome associated with lymph-vascular invasion in colorectal cancer patients and predictive label construction

OBJECTIVE: To identify differences between the composition, abundance, and biological function of the intestinal microbiome of patients with and without lymph-vascular invasion (LVI) colorectal cancer (CRC) and to construct predictive labels to support accurate assessment of LVI in CRC. METHOD: 134...

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Autores principales: Chen, Chuanbin, Chen, Kang, Huang, Zigui, Huang, Xiaoliang, Wang, Zhen, He, Fuhai, Qin, Mingjian, Long, Chenyan, Tang, Binzhe, Mo, Xianwei, Liu, Jungang, Tang, Weizhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10215531/
https://www.ncbi.nlm.nih.gov/pubmed/37249979
http://dx.doi.org/10.3389/fcimb.2023.1098310
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author Chen, Chuanbin
Chen, Kang
Huang, Zigui
Huang, Xiaoliang
Wang, Zhen
He, Fuhai
Qin, Mingjian
Long, Chenyan
Tang, Binzhe
Mo, Xianwei
Liu, Jungang
Tang, Weizhong
author_facet Chen, Chuanbin
Chen, Kang
Huang, Zigui
Huang, Xiaoliang
Wang, Zhen
He, Fuhai
Qin, Mingjian
Long, Chenyan
Tang, Binzhe
Mo, Xianwei
Liu, Jungang
Tang, Weizhong
author_sort Chen, Chuanbin
collection PubMed
description OBJECTIVE: To identify differences between the composition, abundance, and biological function of the intestinal microbiome of patients with and without lymph-vascular invasion (LVI) colorectal cancer (CRC) and to construct predictive labels to support accurate assessment of LVI in CRC. METHOD: 134 CRC patients were included, which were divided into two groups according to the presence or absence of LVI, and their intestinal microbiomes were sequenced by 16SrRNA and analyzed for differences. The transcriptome sequencing data of 9 CRC patients were transformed into immune cells abundance matrix by CIBERSORT algorithm, and the correlation among LVI-associated differential intestinal microbiomes, immune cells, immune-related genes and LVI-associated differential GO items and KEGG pathways were analyzed. A random forest (RF) and eXtreme Gradient Boosting (XGB) model were constructed to predict the LVI of CRC patients based on the differential microbiome. RESULT: There was no significant difference in α-diversity and β-diversity of intestinal microbiome between CRC patients with and without LVI (P > 0.05). Linear discriminant analysis Effect Size (LEfSe) analysis showed 34 intestinal microbiomes enriched in CRC patients of the LVI group and 5 intestinal microbiomes were significantly enriched in CRC patients of the non-lymph-vascular invasion (NLVI) group. The RF and XGB prediction models constructed with the top 15% of the LVI-associated differential intestinal microbiomes ranked by feature significance had good efficacy. CONCLUSIONS: There are 39 intestinal flora with significantly different species abundance between the LVI and NLVI groups. g:Alistipes.s:Alistipes_indistinctus is closely associated with colorectal cancer vascular invasion. LVI-associated differential intestinal flora may be involved in regulating the infiltration of immune cells in CRC and influencing the expression of immune-related genes. LVI-associated differential intestinal flora may influence the process of vascular invasion in CRC through a number of potential biological functions. RF prediction models and XGB prediction models constructed based on microbial markers of gut flora can be used to predict CRC-LVI conditions.
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spelling pubmed-102155312023-05-27 Identification of intestinal microbiome associated with lymph-vascular invasion in colorectal cancer patients and predictive label construction Chen, Chuanbin Chen, Kang Huang, Zigui Huang, Xiaoliang Wang, Zhen He, Fuhai Qin, Mingjian Long, Chenyan Tang, Binzhe Mo, Xianwei Liu, Jungang Tang, Weizhong Front Cell Infect Microbiol Cellular and Infection Microbiology OBJECTIVE: To identify differences between the composition, abundance, and biological function of the intestinal microbiome of patients with and without lymph-vascular invasion (LVI) colorectal cancer (CRC) and to construct predictive labels to support accurate assessment of LVI in CRC. METHOD: 134 CRC patients were included, which were divided into two groups according to the presence or absence of LVI, and their intestinal microbiomes were sequenced by 16SrRNA and analyzed for differences. The transcriptome sequencing data of 9 CRC patients were transformed into immune cells abundance matrix by CIBERSORT algorithm, and the correlation among LVI-associated differential intestinal microbiomes, immune cells, immune-related genes and LVI-associated differential GO items and KEGG pathways were analyzed. A random forest (RF) and eXtreme Gradient Boosting (XGB) model were constructed to predict the LVI of CRC patients based on the differential microbiome. RESULT: There was no significant difference in α-diversity and β-diversity of intestinal microbiome between CRC patients with and without LVI (P > 0.05). Linear discriminant analysis Effect Size (LEfSe) analysis showed 34 intestinal microbiomes enriched in CRC patients of the LVI group and 5 intestinal microbiomes were significantly enriched in CRC patients of the non-lymph-vascular invasion (NLVI) group. The RF and XGB prediction models constructed with the top 15% of the LVI-associated differential intestinal microbiomes ranked by feature significance had good efficacy. CONCLUSIONS: There are 39 intestinal flora with significantly different species abundance between the LVI and NLVI groups. g:Alistipes.s:Alistipes_indistinctus is closely associated with colorectal cancer vascular invasion. LVI-associated differential intestinal flora may be involved in regulating the infiltration of immune cells in CRC and influencing the expression of immune-related genes. LVI-associated differential intestinal flora may influence the process of vascular invasion in CRC through a number of potential biological functions. RF prediction models and XGB prediction models constructed based on microbial markers of gut flora can be used to predict CRC-LVI conditions. Frontiers Media S.A. 2023-05-12 /pmc/articles/PMC10215531/ /pubmed/37249979 http://dx.doi.org/10.3389/fcimb.2023.1098310 Text en Copyright © 2023 Chen, Chen, Huang, Huang, Wang, He, Qin, Long, Tang, Mo, Liu and Tang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cellular and Infection Microbiology
Chen, Chuanbin
Chen, Kang
Huang, Zigui
Huang, Xiaoliang
Wang, Zhen
He, Fuhai
Qin, Mingjian
Long, Chenyan
Tang, Binzhe
Mo, Xianwei
Liu, Jungang
Tang, Weizhong
Identification of intestinal microbiome associated with lymph-vascular invasion in colorectal cancer patients and predictive label construction
title Identification of intestinal microbiome associated with lymph-vascular invasion in colorectal cancer patients and predictive label construction
title_full Identification of intestinal microbiome associated with lymph-vascular invasion in colorectal cancer patients and predictive label construction
title_fullStr Identification of intestinal microbiome associated with lymph-vascular invasion in colorectal cancer patients and predictive label construction
title_full_unstemmed Identification of intestinal microbiome associated with lymph-vascular invasion in colorectal cancer patients and predictive label construction
title_short Identification of intestinal microbiome associated with lymph-vascular invasion in colorectal cancer patients and predictive label construction
title_sort identification of intestinal microbiome associated with lymph-vascular invasion in colorectal cancer patients and predictive label construction
topic Cellular and Infection Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10215531/
https://www.ncbi.nlm.nih.gov/pubmed/37249979
http://dx.doi.org/10.3389/fcimb.2023.1098310
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