Integrative analysis of single-cell sequencing, bulk transcriptomics and experimental verification reveals key molecular mechanisms of vascular smooth muscle cells involved in intracranial aneurysm progression

Background and Purpose Intracranial aneurysm (IA) represents a prevalent cerebrovascular disorder. Although VSMC dysfunction has been implicated as a central contributor to IA pathogenesis, the precise molecular underpinnings remain incompletely elucidated. This study sought to leverage multi-omics integration for characterizing VSMC-associated pivotal genes and their upstream regulatory architectures during IA progression. Methods We obtained IA-related single-cell sequencing dataset GSE193533 and transcriptomic microarray datasets GSE75436 and GSE122897 from the GEO database. Seurat package was used for single-cell analysis. Limma package and WGCNA were used to obtain DEGs and IA-related hub genes. The intersection of the three gene sets was taken to obtain candidate genes, followed by GO and KEGG enrichment analysis. Machine learning methods were applied to screen key genes and construct a diagnostic prediction model. Immune infiltration analysis and ceRNA/transcriptional regulatory networks were performed. CMap and molecular docking predicted therapeutic drugs. Key genes were validated using qRT-PCR and Western blot analysis. Results Compared to the normal group, the proportion of VSMCs gradually decreased in IA tissues. The intersection yielded 113 candidate genes, mainly enriched in neutrophil degranulation, lysosome, and other pathways. Machine learning screened out four key genes: COL5A1, IGFBP2, RASL12, and PLCB4. RT-qPCR and Western blot validation confirmed that COL5A1 and IGFBP2 were significantly upregulated while RASL12 and PLCB4 were significantly downregulated in IA samples at both mRNA and protein levels (P < 0.001). Immune analysis suggested that M0 macrophages and gamma delta T cells were significantly upregulated in the IA group, and key genes were significantly correlated with the infiltration of M2 macrophages and other immune cells. Furthermore, we constructed a ceRNA network centered on KCNQ1OT1 and identified key transcription factors such as SREBF1. Drug prediction yielded five candidate drugs, including carteolol. Key genes were validated in an independent dataset and at the single-cell level. Conclusion This study constructed a multi-omics integrative analysis strategy, revealing the important role of VSMC dysfunction and related molecular events in IA development and progression, and discovered some potential markers and therapeutic targets, providing new insights for the diagnosis and treatment of IA.