Integrative bioinformatics and machine learning identify shared molecular mechanisms and diagnostic biomarkers between Helicobacter pylori infection and atrial fibrillation

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: 15 key feature genes for H. pylori, 23 for AF identified through machine learning

• Primary Outcome: 73 common differentially expressed genes between H. pylori infection and atrial fibrillation

• Nnt Or Sensitivity: S100A8 identified as shared diagnostic biomarker with 10 hub genes including TYROBP, ITGB2, and SPI1

• Confidence Interval: Not reported

Clinical Application

Computational findings need laboratory and clinical validation before implementation Relevant to Australian practice given high H. pylori prevalence and AF burden; potential biomarkers could inform TGA-approved diagnostic development and guide RACGP screening protocols Patients with H. pylori infection at risk for atrial fibrillation, requiring experimental validation

Abstract

BACKGROUND: Helicobacter pylori (H. pylori) infection and atrial fibrillation(AF) are major global health concerns. Emerging evidence has suggested a potentially chronic inflammation-mediated link between them, but the shared genetic mechanisms remain unclear. METHODS: We analyzed multidataset gene expression profiles from the Gene Expression Omnibus (GEO) database. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), functional enrichment, and machine learning were employed to identify common genes, pathways, and diagnostic biomarkers. Protein-protein interaction (PPI) networks, drug-gene analysis, and molecular docking were used to identify hub genes and potential therapeutics. RESULTS: We identified 73 common differentially expressed genes (DEGs) between H. pylori infection and AF, which were predominantly enriched in immune-related processes including leukocyte activation, neutrophil migration, and myeloid cell-mediated immunity. Machine learning identified 15 and 23 key feature genes for H. pylori and AF, respectively, with S100A8 emerging as a shared diagnostic biomarker. Ten hub genes including TYROBP, ITGB2, and SPI1, were identified from the PPI network. Drug repositioning analysis suggested retinoic acid, indirubin, and ropivacaine as candidate therapeutics targeting these key hub genes. CONCLUSION: Our integrative analysis highlights the central role of immune-inflammatory pathways in linking H. pylori infection to AF. We propose S100A8 and other identified hub genes as potential biomarkers and therapeutic targets. The predicted candidate therapeutics, particularly retinoic acid, may offer novel avenues for intervention, warranting further experimental validation.

References

1. 1.Deng, A., & Wang, W. (2024). Integrative bioinformatics and machine learning identify shared molecular mechanisms and diagnostic biomarkers between Helicobacter pylori infection and atrial fibrillation. PLoS One. https://doi.org/10.1371/journal.pone.0346038