Identification of shared molecular biomarkers and pathogenic mechanisms between gastroesophageal reflux disease and ischemic stroke via integrated machine learning.

ABSTRACT: Growing epidemiological evidence suggests a bidirectional relationship between gastroesophageal reflux disease (GERD) and ischemic stroke (IS), yet the shared molecular mechanisms remain poorly characterized. This study aims to identify common biomarkers and elucidate the pathogenic links between GERD and IS using integrative bioinformatics and machine learning approaches. Transcriptomic datasets for GERD (GSE26886 and GSE39491) and IS (GSE22255 and GSE58294) were obtained from the Gene Expression Omnibus. Batch effects were corrected using ComBat, and shared differentially expressed genes were identified via limma. Functional enrichment analyses (gene ontology and Kyoto encyclopedia of genes and genomes) were performed to uncover involved pathways. Key hub genes were selected using 3 mach

SUBMITTER: Huang F 

PROVIDER: S-EPMC12643770 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature