Zhu Y, Geng SY, Chen Y, Ru QJ, Zheng Y, Jiang N, Zhu FY, Zhang YS. Machine learning algorithms reveal gut microbiota signatures associated with chronic hepatitis B-related hepatic fibrosis. World J Gastroenterol 2025; 31(16): 105985 [DOI: 10.3748/wjg.v31.i16.105985]
Corresponding Author of This Article
Yong-Sheng Zhang, PhD, Doctor, School of Basic Medical Sciences, Zhejiang Chinese Medical University, No. 548 Binwen Road, Binjiang District, Hangzhou 310053, Zhejiang Province, China. alex.yszhang@zcmu.edu.cn
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Retrospective Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. Apr 28, 2025; 31(16): 105985 Published online Apr 28, 2025. doi: 10.3748/wjg.v31.i16.105985
Machine learning algorithms reveal gut microbiota signatures associated with chronic hepatitis B-related hepatic fibrosis
Ying Zhu, Shi-Yu Geng, Yao Chen, Qing-Jing Ru, Yi Zheng, Na Jiang, Fei-Ye Zhu, Yong-Sheng Zhang
Ying Zhu, Shi-Yu Geng, Fei-Ye Zhu, Yong-Sheng Zhang, School of Basic Medical Sciences, Zhejiang Chinese Medical University, Hangzhou 310053, Zhejiang Province, China
Yao Chen, National Key Laboratory of Immunity and Inflammation Suzhou Institute of Systems Medicine Chinese Academy of Medical Sciences and Peking Union Medical College, Suzhou 215123, Jiangsu Province, China
Qing-Jing Ru, Yi Zheng, Na Jiang, Department of Infectious Disease, The Second Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310005, Zhejiang Province, China
Author contributions: Zhu Y and Zhang YS conceptualized the study; Zhu Y, Geng SY, and Chen Y developed the methodology; Zhu Y and Geng SY wrote the original draft; Ru QJ, Zheng Y, and Jiang N were responsible for data curation; Ru QJ and Zheng Y conducted the investigation; Ru QJ, Zheng Y, Jiang N, and Zhang YS managed project administration; Supervision was provided by Ru QJ, Zheng Y, Jiang N, Zhu FY, and Zhang YS; Jiang N performed the formal analysis; Zhu FY and Zhang YS contributed to writing, review, and editing; Zhang YS acquired funding and provided resources.
Supported by the Zhejiang Provincial Natural Science Foundation, No. LZ22H270001.
Institutional review board statement: This study was approved by the Ethics Committee of Second Hospital of Zhejiang Chinese Medical University (No. Lun Shen 2023 Yan No. 015-IH01). All the study procedures were performed in accordance with the tenets of the Declaration of Helsinki.
Informed consent statement: Before formally entering the study, all participants signed written informed consent forms.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: All data have been uploaded as supplementary data.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yong-Sheng Zhang, PhD, Doctor, School of Basic Medical Sciences, Zhejiang Chinese Medical University, No. 548 Binwen Road, Binjiang District, Hangzhou 310053, Zhejiang Province, China. alex.yszhang@zcmu.edu.cn
Received: February 13, 2025 Revised: March 17, 2025 Accepted: April 9, 2025 Published online: April 28, 2025 Processing time: 73 Days and 21.9 Hours
Core Tip
Core Tip: This study employs machine learning to identify gut microbiota signatures associated with hepatic fibrosis (HF) in chronic hepatitis B (CHB). Key findings reveal Dorea as a pivotal microbial marker, with its abundance inversely correlated to HF severity and linked to liver function indicators (γ-glutamyl transferase, alkaline phosphatase, total bilirubin, aspartate aminotransferase/alanine transaminase). Using advanced machine learning models such as eXtreme gradient boosting and random forest, we reveal dysregulated metabolic pathways contributing to HF progression, emphasizing gut-liver axis interactions. These results highlight Dorea as a potential biomarker for early HF detection and a therapeutic target, advancing non-invasive diagnostic strategies and microbiome-based interventions for CHB-related fibrosis.
