{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T04:36:25Z","timestamp":1792470985659,"version":"4.5.0"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cell. Infect. Microbiol."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>\n                      <jats:italic>Helicobacter pylori<\/jats:italic>\n                      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\n                      <jats:italic>H. pylori<\/jats:italic>\n                      infection and the GM and to evaluate how microbiome alterations relate to clinical outcomes such as gastritis, ulcers, and gastric cancer.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The analysis revealed significant microbial shifts associated with\n                      <jats:italic>H. pylori<\/jats:italic>\n                      infection, including enrichment of\n                      <jats:italic>Proteobacteria, Fusobacterium<\/jats:italic>\n                      spp., and\n                      <jats:italic>Prevotella<\/jats:italic>\n                      spp., and depletion of beneficial taxa like\n                      <jats:italic>Lactobacillus<\/jats:italic>\n                      spp. and\n                      <jats:italic>Faecalibacterium prausnitzii<\/jats:italic>\n                      . 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\n                      <jats:italic>Fusobacterium<\/jats:italic>\n                      spp. and\n                      <jats:italic>Bacteroides fragilis<\/jats:italic>\n                      with strong predictive power.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>\n                      This study highlights the critical role of GM dysbiosis in\n                      <jats:italic>H. pylori<\/jats:italic>\n                      -related disease progression. The identified microbial signatures and predictive models offer promising tools for early diagnosis, risk stratification, and personalized treatment of\n                      <jats:italic>H. pylori<\/jats:italic>\n                      -associated gastrointestinal disorders. Future integration of multi-omics data may further unravel the microbial mechanisms and support microbiome-based precision medicine.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcimb.2025.1610523","type":"journal-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T04:10:25Z","timestamp":1754021425000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Meta-analysis of H. pylori and the gut microbiome interactions and clinical outcomes"],"prefix":"10.3389","volume":"15","author":[{"given":"Xiongjian","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haiyan","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lixing","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1663","DOI":"10.1093\/bib\/bbz103","article-title":"Computational\/in silico methods in drug target and lead prediction","volume":"21","author":"Agamah","year":"2020","journal-title":"Briefings Bioinf."},{"key":"B2","doi-asserted-by":"publisher","first-page":"2890","DOI":"10.1093\/rfs\/hhy105","article-title":"Approaching mean-variance efficiency for large portfolios","volume":"32","author":"Ao","year":"2019","journal-title":"Rev. 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