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Analysis of bacterial diversity and community structure in gastric juice of patients with advanced gastric cancer
BACKGROUND: The occurrence and development of gastric cancer are related to microorganisms, which can be used as potential biomarkers of gastric cancer. OBJECTIVE: To screen the microbiological markers of gastric cancer from the microorganisms of gastric juice. METHODS: Gastric juice samples were co...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860007/ https://www.ncbi.nlm.nih.gov/pubmed/36662326 http://dx.doi.org/10.1007/s12672-023-00612-7 |
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author | Wei, Qiang Zhang, Qi Wu, Yinhang Han, Shuwen Yin, Lei Zhang, Jinyu Gao, Yuhai Shen, Hong Zhuang, Jing Chu, Jian Liu, Jiang Wei, Yunhai |
author_facet | Wei, Qiang Zhang, Qi Wu, Yinhang Han, Shuwen Yin, Lei Zhang, Jinyu Gao, Yuhai Shen, Hong Zhuang, Jing Chu, Jian Liu, Jiang Wei, Yunhai |
author_sort | Wei, Qiang |
collection | PubMed |
description | BACKGROUND: The occurrence and development of gastric cancer are related to microorganisms, which can be used as potential biomarkers of gastric cancer. OBJECTIVE: To screen the microbiological markers of gastric cancer from the microorganisms of gastric juice. METHODS: Gastric juice samples were collected from 61 healthy people and 78 patients with gastric cancer (48 cases of early gastric cancer and 30 cases of advanced gastric cancer). The bacterial 16 S rRNA V1-V4 region of gastric juice samples was sequenced. The Shannon index, Simpson index, Ace index and Chao index were used to analyze the diversity of gastric juice samples. The RDP classifier Bayesian algorithm was used to analyze the community structure of 97% OTU representative sequences with similar levels. Linear discriminant analysis and ST-test were used to analyze the differences. Six machine learning algorithms, including the logistic regression algorithm, random forest algorithm, neural network algorithm, support vector machine algorithm, Catboost algorithm and gradient lifting tree algorithm, were used to construct risk prediction models for gastric cancer and advanced gastric cancer. RESULTS: The microbiota diversity and the abundance of bacteria was different in the healthy group, early gastric cancer and advanced gastric cancer (P < 0.05). The top five abundant bacteria among the three groups were Streptococcus, Rhodococcus, Prevotella, Pseudomonas and Helicobacter. Bacterial flora such as Streptococcus, Rhodococcus and Ochrobactrum were significantly different between the healthy group and the gastric cancer group. The accuracy of the random forest prediction model is the highest (82.73% correct). The bacteria with the highest predictive value included Streptococcus, Lactobacillus and Ochrobactrum. The abundance of bacteria such as Fusobacterium, Capnocytophaga, Atopobium, Corynebacterium was high in the advanced gastric cancer group. CONCLUSION: Gastric juice bacteria can be used as potential biomarkers to predict the occurrence and development of gastric cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-023-00612-7 |
format | Online Article Text |
id | pubmed-9860007 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-98600072023-01-22 Analysis of bacterial diversity and community structure in gastric juice of patients with advanced gastric cancer Wei, Qiang Zhang, Qi Wu, Yinhang Han, Shuwen Yin, Lei Zhang, Jinyu Gao, Yuhai Shen, Hong Zhuang, Jing Chu, Jian Liu, Jiang Wei, Yunhai Discov Oncol Research BACKGROUND: The occurrence and development of gastric cancer are related to microorganisms, which can be used as potential biomarkers of gastric cancer. OBJECTIVE: To screen the microbiological markers of gastric cancer from the microorganisms of gastric juice. METHODS: Gastric juice samples were collected from 61 healthy people and 78 patients with gastric cancer (48 cases of early gastric cancer and 30 cases of advanced gastric cancer). The bacterial 16 S rRNA V1-V4 region of gastric juice samples was sequenced. The Shannon index, Simpson index, Ace index and Chao index were used to analyze the diversity of gastric juice samples. The RDP classifier Bayesian algorithm was used to analyze the community structure of 97% OTU representative sequences with similar levels. Linear discriminant analysis and ST-test were used to analyze the differences. Six machine learning algorithms, including the logistic regression algorithm, random forest algorithm, neural network algorithm, support vector machine algorithm, Catboost algorithm and gradient lifting tree algorithm, were used to construct risk prediction models for gastric cancer and advanced gastric cancer. RESULTS: The microbiota diversity and the abundance of bacteria was different in the healthy group, early gastric cancer and advanced gastric cancer (P < 0.05). The top five abundant bacteria among the three groups were Streptococcus, Rhodococcus, Prevotella, Pseudomonas and Helicobacter. Bacterial flora such as Streptococcus, Rhodococcus and Ochrobactrum were significantly different between the healthy group and the gastric cancer group. The accuracy of the random forest prediction model is the highest (82.73% correct). The bacteria with the highest predictive value included Streptococcus, Lactobacillus and Ochrobactrum. The abundance of bacteria such as Fusobacterium, Capnocytophaga, Atopobium, Corynebacterium was high in the advanced gastric cancer group. CONCLUSION: Gastric juice bacteria can be used as potential biomarkers to predict the occurrence and development of gastric cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s12672-023-00612-7 Springer US 2023-01-20 /pmc/articles/PMC9860007/ /pubmed/36662326 http://dx.doi.org/10.1007/s12672-023-00612-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Wei, Qiang Zhang, Qi Wu, Yinhang Han, Shuwen Yin, Lei Zhang, Jinyu Gao, Yuhai Shen, Hong Zhuang, Jing Chu, Jian Liu, Jiang Wei, Yunhai Analysis of bacterial diversity and community structure in gastric juice of patients with advanced gastric cancer |
title | Analysis of bacterial diversity and community structure in gastric juice of patients with advanced gastric cancer |
title_full | Analysis of bacterial diversity and community structure in gastric juice of patients with advanced gastric cancer |
title_fullStr | Analysis of bacterial diversity and community structure in gastric juice of patients with advanced gastric cancer |
title_full_unstemmed | Analysis of bacterial diversity and community structure in gastric juice of patients with advanced gastric cancer |
title_short | Analysis of bacterial diversity and community structure in gastric juice of patients with advanced gastric cancer |
title_sort | analysis of bacterial diversity and community structure in gastric juice of patients with advanced gastric cancer |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860007/ https://www.ncbi.nlm.nih.gov/pubmed/36662326 http://dx.doi.org/10.1007/s12672-023-00612-7 |
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