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

Abstract

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.

Keywords: Chronic hepatitis B virus infection; Fecal microbiomes; Hepatic fibrosis; Liver stiffness; Serum intestinal mucosal barrier.

Figure 1

Flow chart of sample inclusion, exclusion and final grouping for chronic hepatitis B patients. CHB: Chronic hepatitis B; HF: Hepatic fibrosis.

Figure 2

Identification of differential gut microbiota features in non-hepatic fibrosis and hepatic fibrosis groups. The relative abundance in non-hepatic fibrosis and hepatic fibrosis groups. A: Cyanobacteria; B: Acidobacteria; C: Thermophilic bacteria; D: Dorea; E: Lachnospira; F: The Dorea/Firmicutes ratio. All data were analyzed by the Mann-Whitney U test. ^aP < 0.05. ^bP < 0.01. ^cP < 0.001. HF: Hepatic fibrosis.

Figure 3

Identification of differential gut microbiota features in patients in mild hepatic fibrosis and significant hepatic fibrosis groups and different stages of hepatic fibrosis. A-C: The relative abundance at the phylum level: Firmicutes (A); Verrucomicrobia (B); Acidobacteria (C); D: The relative abundance of Parabacteroides at the genus level; E: The Parabacteroides/Bacteroidetes ratio in mild hepatic fibrosis and significant hepatic fibrosis groups; F-H: The relative abundance at the phylum level: Spirochaetes (F); Ruminococcaceae Ruminococcus (G); Dorea (H); I: The Dorea/Firmicutes ratio in different stages of hepatic fibrosis. All data were analyzed by the Mann-Whitney U test. ^aP < 0.05. ^bP < 0.01. ^cP < 0.001. mHF: Mild hepatic fibrosis; sHF: Significant hepatic fibrosis.

Figure 4

Linear discriminant analysis effect size analysis identified differential microbiota in hepatic fibrosis patients. A and B: Differential microbiota in hepatic fibrosis (HF) and non-HF groups; C and D: Differential microbiota in mild HF and significant HF groups; E and F: Differential microbiota in different fibrosis stages (F1, F2, F3, and F4). All data were analyzed by linear discriminant analysis effect size analysis (linear discriminant analysis > 2, P < 0.05). LDA: Linear discriminant analysis; HF: Hepatic fibrosis; mHF: Mild hepatic fibrosis; sHF: Significant hepatic fibrosis.

Figure 5

The relationship between gut microbiota, clinical indicators, and intestinal barrier function in different stages of hepatic fibrosis patients. A: The correlation analyses among gut microbiota, clinical indicators, and intestinal barrier function in hepatic fibrosis (HF) patients; B: The serum levels of γ-glutamyl transferase in different stages of HF patients; C-E: The serum levels of intestinal barrier function in different stages of HF patients. ^aP < 0.05. ^bP < 0.01. ^cP < 0.001. ZO-1: Zonula occludens-1; AST: Aspartate aminotransferase; ALT: Alanine transaminase; ALB: Albumin; TBIL: Total bilirubin; IBIL: Indirect bilirubin; ALP: Alkaline phosphatase; WBC: White blood cell; BA: Basophil; PLT: Platelet; GGT: γ-glutamyl transferase; HF: Hepatic fibrosis; mHF: Mild hepatic fibrosis; sHF: Significant hepatic fibrosis.

Figure 6

The relationship between key signaling pathways and hepatic fibrosis progression. A: The differential microbiota metabolic pathways between non-hepatic fibrosis (HF) and HF groups in Kyoto Encyclopedia of Genes and Genomes (KEGG) databases; B and C: The differential microbiota metabolic pathways between mild HF (mHF) and significant HF (sHF) groups in KEGG and MetaCyc databases, respectively; D: The subgroup analysis of mHF and sHF revealed 24 differential pathways in KEGG and MetaCyc databases. t-test was used to assess the significance of differences in microbial community functioning between different subgroups. HF: Hepatic fibrosis; mHF: Mild hepatic fibrosis; sHF: Significant hepatic fibrosis; TCA: Tricarboxylic acid; KEGG: Kyoto Encyclopedia of Genes and Genomes.

Figure 7

Machine learning-based identification of key gut microbiota in hepatic fibrosis. A and B: The greatest differences in relative abundance between the hepatic fibrosis (HF) and non-HF groups according to the eXtreme gradient boosting (XGBoost) machine learning algorithm of top 10 phyla (A) and genera (B); C and D: The greatest differences in relative abundance between the mild HF and significant HF groups according to the XGBoost algorithm of top 10 phyla (C) and genera (D); E-G: The top 20 significant signatures of gut microbiota at the genus level according to the random forest algorithm. SHAP: Shapley additive explanations; HF: Hepatic fibrosis; mHF: Mild hepatic fibrosis; sHF: Significant hepatic fibrosis.