Machine learning algorithms reveal gut microbiota signatures associated with chronic hepatitis B-related hepatic fibrosis

• Published in: World journal of gastroenterology : WJG Vol. 31; no. 16; p. 105985
• Main Authors: Zhu, Ying, Geng, Shi-Yu, Chen, Yao, Ru, Qing-Jing, Zheng, Yi, Jiang, Na, Zhu, Fei-Ye, Zhang, Yong-Sheng
• Format: Journal Article
• Language: English
• Published: United States Baishideng Publishing Group Inc 28.04.2025
• Subjects: Adult; Algorithms; Disease Progression; Feces - microbiology; Female; Gastrointestinal Microbiome - genetics; Hepatitis B, Chronic - complications; Hepatitis B, Chronic - microbiology; Humans; Liver - diagnostic imaging; Liver - pathology; Liver Cirrhosis - diagnosis; Liver Cirrhosis - etiology; Liver Cirrhosis - microbiology; Liver Cirrhosis - pathology; Liver Cirrhosis - virology; Machine Learning; Male; Middle Aged; Retrospective Study; RNA, Ribosomal, 16S - genetics; Chronic hepatitis B virus infection; Hepatic fibrosis; Fecal microbiomes; Liver stiffness; Serum intestinal mucosal barrier
• ISSN: 1007-9327; 2219-2840; 2219-2840
• DOI: 10.3748/wjg.v31.i16.105985

BACKGROUND Hepatic fibrosis (HF) represents a pivotal stage in the progression and potential reversal of cirrhosis, underscoring the importance of early identification and therapeutic intervention to modulate disease trajectory. AIM To explore the complex relationship between chronic hepatitis B (CHB)-related HF and gut microbiota to identify microbiota signatures significantly associated with HF progression in CHB patients using advanced machine learning algorithms. METHODS This study included patients diagnosed with CHB and classified them into HF and non-HF groups based on liver stiffness measurements. The HF group was further subdivided into four subgroups: F1, F2, F3, and F4. Data on clinical indicators were collected. Stool samples were collected for 16S rRNA sequencing to assess the gut microbiome. Microbiota diversity, relative abundance, and linear discriminant analysis effect size (LEfSe) were analyzed in different groups. Correlation analysis between clinical indicators and the relative abundance of gut microbiota was performed. The random forest and eXtreme gradient boosting algorithms were used to identify key differential gut microbiota. The Shapley additive explanations were used to evaluate microbiota importance. RESULTS Integrating the results from univariate analysis, LEfSe, and machine learning, we identified that the presence of Dorea in gut microbiota may be a key feature associated with CHB-related HF. Dorea possibly serves as a core differential feature of the gut microbiota that distinguishes HF from non-HF patients, and the presence of Dorea shows significant variations across different stages of HF (P < 0.05). The relative abundance of Dorea significantly decreases with increasing HF severity (P = 0.041). Moreover, the gut microbiota composition in patients with different stages of HF was found to correlate with several liver function indicators, such as γ-glutamyl transferase, alkaline phosphatase, total bilirubin, and the aspartate aminotransferase/alanine transaminase ratio (P < 0.05). The associated pathways were predominantly enriched in biosynthesis, degradation/utilization/assimilation, generation of precursors, metabolites, and energy, among other categories. CONCLUSION HF affects the composition of the gut microbiota, indicating that the gut microbiota plays a crucial role in its pathophysiological processes. The abundance of Dorea varies significantly across various stages of HF, making it a potential microbial marker for identifying HF onset and progression.