Identifying and validating key genes mediating intracranial aneurysm rupture using weighted correlation network analysis and exploration of personalized treatment.

Background: Intracranial aneurysmal subarachnoid hemorrhage (aSAH) is a dangerous and highly fatal condition if ruptured. Significant advances have been made in the treatment of unruptured intracranial aneurysms (UIAs), but risk assessment methods for early diagnosis of intracranial aneurysm (IA) rupture remain limited. Methods: The datasets of IA GSE13353, GSE15629, and GSE54083 were downloaded through the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in unruptured and ruptured aneurysms were identified by R software using methods such as gene set enrichment analysis (GSEA) and weighted gene co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed on the DEGs, and logistic regression models were used to construct a prediction model to discriminate UIA from healthy samples. We then performed GSEA on the genes in the model, followed by model validation using the GSE54083 dataset. Finally, we used the single-sample (ss)GSEA method to investigate the relationship between the diagnostic model genes and immune cells and immune function. Results: A total of 79 DEGs were obtained in patients with IA rupture compared to unruptured controls. The results of KEGG and GO enrichment analysis showed that neutrophil activation is involved in immune response, neutrophil mediated immunity, and positive regulation of angiogenesis. Interestingly, the results of immunoassays demonstrated that the break in IA may be associated with immune T cells. We used DEGs and WGCNA to determine common genes. The logistic regression model was trained based on 24 intersecting genes, and eventually retained 2 genes, Conclusions: We identified new diagnostic genes associated with IA rupture, which may provide a new way of aneurysm diagnosis.