Abstract
INTRODUCTION: Helicobacter pylori is a globally prevalent gastric pathogen associated with chronic gastritis, peptic ulcers, and gastric cancer. Its interaction with the gut microbiome (GM), a dynamic microbial community within the gastrointestinal tract, plays a critical role in modulating host immune responses and disease progression. This study aimed to investigate the complex interactions between H. pylori infection and the GM and to evaluate how microbiome alterations relate to clinical outcomes such as gastritis, ulcers, and gastric cancer. METHODS: A meta-analysis was conducted using publicly available 16S rRNA and shotgun metagenomic datasets. Microbiome composition differences were assessed using differential abundance analysis, alpha- and beta-diversity metrics, and principal component analysis (PCA). Random forest models were employed to predict the clinical outcomes based on microbiome and clinical data. Hyperparameter tuning and cross-validation were applied to ensure model robustness. RESULTS: The analysis revealed significant microbial shifts associated with H. pylori infection, including enrichment of Proteobacteria, Fusobacterium spp., and Prevotella spp., and depletion of beneficial taxa like Lactobacillus spp. and Faecalibacterium prausnitzii. Microbial diversity declined progressively with disease severity. Predictive models demonstrated high accuracy (89.3%) in classifying the disease states and identifying key microbial biomarkers such as Fusobacterium spp. and Bacteroides fragilis with strong predictive power. DISCUSSION: This study highlights the critical role of GM dysbiosis in H. pylori-related disease progression. The identified microbial signatures and predictive models offer promising tools for early diagnosis, risk stratification, and personalized treatment of H. pylori-associated gastrointestinal disorders. Future integration of multi-omics data may further unravel the microbial mechanisms and support microbiome-based precision medicine.