Investigating the Mechanisms and Potential Therapeutic Targets of Vestibular Migraine

BackgroundVestibular Migraine (VM) is a complex neurological disorder with recurrent headaches and various vestibular symptoms. Although it severely affects patients quality of life, its causes and pathophysiology are still unclear, and effective treatments are scarce. The lack of data emphasizes the need for bioinformatics to find key genes and pathways in VM, which could help develop new diagnostic and treatment methods. MethodThe GSE109558 dataset was acquired from the Gene Expression Omnibus (GEO) database. To identify VM - related differentially expressed genes, screening was carried out through limma and Weighted Correlation Network Analysis (WGCNA).The functional analysis of VM - related differentially expressed genes was conducted using three bioinformatics approaches: Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and Gene Set Enrichment Analysis (GSEA). Feature selection was further refined using lasso regression and random forest. Also, CIBERSORT was utilized to analyze the infiltration of immune cells, and Spearmans correlation analysis was employed to explore the correlations between diagnostic differentially expressed genes and immune cells.Finally, the Comparative Toxicogenomics Database (CTD) was utilized to search for corresponding drugs, and molecular docking was performed to explore potential therapeutic targets. ResultSix key feature genes (CABIN1, IFIT3, HEATR1, ARHGDIA, RAB11FIP4, and ZNF444) were identified as potential diagnostic markers for VM. Among these, CABIN1 demonstrated the highest diagnostic potential based on ROC curve performance, highlighting its promise as a diagnostic biomarker.Functional annotation of DEGs revealed their enrichment in biological processes related to inflammation, calcium ion channel regulation, and other pathways likely involved in VM pathophysiology. Through the CTD, drugs like Acetaminophen, bisphenol A, and Phenylephrine were identified. Molecular docking simulation was used to explore their potential therapeutic mechanisms for VM. ConclusionThis study offers important insights into the molecular mechanisms of VM and identifies six key feature genes, with CABIN1 standing out as a potential diagnostic marker.These findings pave the way for further research to validate the diagnostic and therapeutic implications of these genes and pathways.