Gut microbes on the risk of advanced adenomas.

Humans Gastrointestinal Microbiome / genetics Bacteria / genetics Adenoma / microbiology Feces / microbiology Colorectal Neoplasms / microbiology Male Middle Aged Female Viruses / isolation & purification High-Throughput Nucleotide Sequencing Aged Machine Learning
Advanced adenomas Enteroviruses Gut bacteria Metagenomic sequencing Prediction model

Journal

BMC microbiology
ISSN: 1471-2180
Titre abrégé: BMC Microbiol
Pays: England
ID NLM: 100966981

Informations de publication

Date de publication:
18 Jul 2024
Historique:
received: 07 04 2024
accepted: 08 07 2024
medline: 19 7 2024
pubmed: 19 7 2024
entrez: 18 7 2024
Statut: epublish

Résumé

More than 90% of colorectal cancer (CRC) arises from advanced adenomas (AA) and gut microbes are closely associated with the initiation and progression of both AA and CRC. To analyze the characteristic microbes in AA. Fecal samples were collected from 92 AA and 184 negative control (NC). Illumina HiSeq X sequencing platform was used for high-throughput sequencing of microbial populations. The sequencing results were annotated and compared with NCBI RefSeq database to find the microbial characteristics of AA. R-vegan package was used to analyze α diversity and β diversity. α diversity included box diagram, and β diversity included Principal Component Analysis (PCA), principal co-ordinates analysis (PCoA), and non-metric multidimensional scaling (NMDS). The AA risk prediction models were constructed based on six kinds of machine learning algorithms. In addition, unsupervised clustering methods were used to classify bacteria and viruses. Finally, the characteristics of bacteria and viruses in different subtypes were analyzed. The abundance of Prevotella sp900557255, Alistipes putredinis, and Megamonas funiformis were higher in AA, while the abundance of Lilyvirus, Felixounavirus, and Drulisvirus were also higher in AA. The Catboost based model for predicting the risk of AA has the highest accuracy (bacteria test set: 87.27%; virus test set: 83.33%). In addition, 4 subtypes (B1V1, B1V2, B2V1, and B2V2) were distinguished based on the abundance of gut bacteria and enteroviruses (EVs). Escherichia coli D, Prevotella sp900557255, CAG-180 sp000432435, Phocaeicola plebeiuA, Teseptimavirus, Svunavirus, Felixounavirus, and Jiaodavirus are the characteristic bacteria and viruses of 4 subtypes. The results of Catboost model indicated that the accuracy of prediction improved after incorporating subtypes. The accuracy of discovery sets was 100%, 96.34%, 100%, and 98.46% in 4 subtypes, respectively. Prevotella sp900557255 and Felixounavirus have high value in early warning of AA. As promising non-invasive biomarkers, gut microbes can become potential diagnostic targets for AA, and the accuracy of predicting AA can be improved by typing.

Sections du résumé

BACKGROUND BACKGROUND
More than 90% of colorectal cancer (CRC) arises from advanced adenomas (AA) and gut microbes are closely associated with the initiation and progression of both AA and CRC.
OBJECTIVE OBJECTIVE
To analyze the characteristic microbes in AA.
METHODS METHODS
Fecal samples were collected from 92 AA and 184 negative control (NC). Illumina HiSeq X sequencing platform was used for high-throughput sequencing of microbial populations. The sequencing results were annotated and compared with NCBI RefSeq database to find the microbial characteristics of AA. R-vegan package was used to analyze α diversity and β diversity. α diversity included box diagram, and β diversity included Principal Component Analysis (PCA), principal co-ordinates analysis (PCoA), and non-metric multidimensional scaling (NMDS). The AA risk prediction models were constructed based on six kinds of machine learning algorithms. In addition, unsupervised clustering methods were used to classify bacteria and viruses. Finally, the characteristics of bacteria and viruses in different subtypes were analyzed.
RESULTS RESULTS
The abundance of Prevotella sp900557255, Alistipes putredinis, and Megamonas funiformis were higher in AA, while the abundance of Lilyvirus, Felixounavirus, and Drulisvirus were also higher in AA. The Catboost based model for predicting the risk of AA has the highest accuracy (bacteria test set: 87.27%; virus test set: 83.33%). In addition, 4 subtypes (B1V1, B1V2, B2V1, and B2V2) were distinguished based on the abundance of gut bacteria and enteroviruses (EVs). Escherichia coli D, Prevotella sp900557255, CAG-180 sp000432435, Phocaeicola plebeiuA, Teseptimavirus, Svunavirus, Felixounavirus, and Jiaodavirus are the characteristic bacteria and viruses of 4 subtypes. The results of Catboost model indicated that the accuracy of prediction improved after incorporating subtypes. The accuracy of discovery sets was 100%, 96.34%, 100%, and 98.46% in 4 subtypes, respectively.
CONCLUSION CONCLUSIONS
Prevotella sp900557255 and Felixounavirus have high value in early warning of AA. As promising non-invasive biomarkers, gut microbes can become potential diagnostic targets for AA, and the accuracy of predicting AA can be improved by typing.

Identifiants

DOI: 10.1186/s12866-024-03416-z PMID: 39026166
pubmed: 39026166
doi: 10.1186/s12866-024-03416-z
pii: 10.1186/s12866-024-03416-z
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

264

Subventions

Organisme : Zhejiang Medical and Health Technology Project
ID : No.2023KY1178
Organisme : Zhejiang Medical and Health Technology Project
ID : No.2023KY1178
Organisme : China University Industry University Research Innovation Fund
ID : No.2023HT078
Organisme : China University Industry University Research Innovation Fund
ID : No.2023HT078
Organisme : PublicWelfare Technology Application Research Program of Huzhou
ID : No. 2023GY18
Organisme : PublicWelfare Technology Application Research Program of Huzhou
ID : No. 2023GY18

Informations de copyright

© 2024. The Author(s).

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Auteurs

Zhuang Jing (Z)

Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang Province, China.
Fifth School of Clinical Medicine of Zhejiang Chinese Medical University (Huzhou Central Hospital), Huzhou, Zhejiang Province, China.
Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer, Huzhou, Zhejiang Province, China.

Wu Zheng (W)

Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang Province, China.
Fifth School of Clinical Medicine of Zhejiang Chinese Medical University (Huzhou Central Hospital), Huzhou, Zhejiang Province, China.
Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer, Huzhou, Zhejiang Province, China.

Song Jianwen (S)

Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang Province, China.
Fifth School of Clinical Medicine of Zhejiang Chinese Medical University (Huzhou Central Hospital), Huzhou, Zhejiang Province, China.
Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer, Huzhou, Zhejiang Province, China.

Shen Hong (S)

Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang Province, China.

Yu Xiaojian (Y)

Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang Province, China.
Fifth School of Clinical Medicine of Zhejiang Chinese Medical University (Huzhou Central Hospital), Huzhou, Zhejiang Province, China.
Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer, Huzhou, Zhejiang Province, China.

Wei Qiang (W)

Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang Province, China.
Fifth School of Clinical Medicine of Zhejiang Chinese Medical University (Huzhou Central Hospital), Huzhou, Zhejiang Province, China.
Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer, Huzhou, Zhejiang Province, China.

Yin Yunfeng (Y)

Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang Province, China.
Fifth School of Clinical Medicine of Zhejiang Chinese Medical University (Huzhou Central Hospital), Huzhou, Zhejiang Province, China.
Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer, Huzhou, Zhejiang Province, China.

Wu Xinyue (W)

Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang Province, China.
Fifth School of Clinical Medicine of Zhejiang Chinese Medical University (Huzhou Central Hospital), Huzhou, Zhejiang Province, China.
Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer, Huzhou, Zhejiang Province, China.

Han Shuwen (H)

Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang Province, China. shuwenhan985@163.com.
Fifth School of Clinical Medicine of Zhejiang Chinese Medical University (Huzhou Central Hospital), Huzhou, Zhejiang Province, China. shuwenhan985@163.com.
Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer, Huzhou, Zhejiang Province, China. shuwenhan985@163.com.
ICL, Junia, Université Catholique de Lille, Lille, France. shuwenhan985@163.com.

Zhao Feimin (Z)

Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang Province, China. zhaofeiminhuzhou@163.com.
Fifth School of Clinical Medicine of Zhejiang Chinese Medical University (Huzhou Central Hospital), Huzhou, Zhejiang Province, China. zhaofeiminhuzhou@163.com.
Key Laboratory of Multiomics Research and Clinical Transformation of Digestive Cancer, Huzhou, Zhejiang Province, China. zhaofeiminhuzhou@163.com.

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Classifications MeSH

questionsmedicales.fr › Eucaryotes › Animaux › Chordés › Vertébrés › Mammifères › Eutheria › Primates › Haplorhini › Catarrhini › Hominidae › Humains › Gut microbes on the risk of advanced adenomas.
questionsmedicales.fr › Environnement et santé publique › Environnement › Écosystème › Biodiversité › Biote › Microbiote › Microbiome gastro-intestinal › Gut microbes on the risk of advanced adenomas.
questionsmedicales.fr › Bactéries › Gut microbes on the risk of advanced adenomas.
questionsmedicales.fr › Tumeurs › Tumeurs par type histologique › Tumeurs épithéliales épidermoïdes et glandulaires › Adénomes › Gut microbes on the risk of advanced adenomas.
questionsmedicales.fr › Liquides et sécrétions biologiques › Fèces › Gut microbes on the risk of advanced adenomas.
questionsmedicales.fr › Maladies de l'appareil digestif › Maladies gastro-intestinales › Maladies intestinales › Maladies du rectum › Tumeurs colorectales › Gut microbes on the risk of advanced adenomas.
questionsmedicales.fr › Personnes › Tranches d'âge › Adulte › Adulte d'âge moyen › Gut microbes on the risk of advanced adenomas.
questionsmedicales.fr › Virus › Gut microbes on the risk of advanced adenomas.
questionsmedicales.fr › Techniques d'investigation › Techniques génétiques › Analyse de séquence › Séquençage nucléotidique à haut débit › Gut microbes on the risk of advanced adenomas.
questionsmedicales.fr › Personnes › Tranches d'âge › Adulte › Sujet âgé › Gut microbes on the risk of advanced adenomas.
questionsmedicales.fr › Sciences de l'information › Méthodologies informatiques › Algorithmes › Intelligence artificielle › Apprentissage machine › Gut microbes on the risk of advanced adenomas.