Screening of immune-related biological markers for aneurysmal subarachnoid hemorrhage based on machine learning approaches.

Background: Aneurysmal subarachnoid hemorrhage (aSAH) is a common hemorrhagic condition frequently encountered in the emergency department, which is characterized by high mortality and disability rates. However, the precise molecular mechanisms underlying the rupture of an aneurysm are still not fully understood. The primary objective of this study is to elucidate the fundamental molecular mechanisms underlying aSAH and provide novel therapeutic targets for the treatment of aSAH. Methods: The gene expression matrix of aSAH was downloaded from the Gene Expression Omnibus (GEO) database. In this study, we employed weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis (DEGs) screening to identify crucial modules and genes associated with aSAH. Furthermore, the evaluation of immune cell infiltration was conducted through the utilization of the single-sample gene set enrichment analysis (ssGSEA) technique and the CIBERSORT algorithm. The study utilized Gene Set Variation Analysis (GSVA), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) to investigate and comprehend the fundamental biological pathways and mechanisms. Results: Using WGCNA, six gene co-expression modules were constructed. Among the identified modules, the yellow module, which encompasses 184 genes, demonstrated the most significant correlation with aSAH. Consequently, it was determined to be the central module responsible for governing the pathogenesis of aSAH. Additionally, the application of WGCNA, LASSO regression, and multiple factor logistic regression analysis revealed ARHGAP26 and SLMAP as the key genes associated with aSAH. Furthermore, the diagnostic efficacy of these pivotal genes in aSAH was confirmed through the use of receiver operating characteristic (ROC) curve analysis, validating their discriminative potential. Moreover, the utilization of GO and KEGG pathway analysis revealed a significant enrichment of inflammation-related signaling in aSAH. Conclusion: The genes ARHGAP26 and SLMAP were identified as significant predictors of aSAH. Accordingly, these genes demonstrate significant potential to function as novel biological markers and therapeutic targets for aSAH.