Identification of key therapeutic targets in nicotine-induced intracranial aneurysm through integrated bioinformatics and machine learning approaches.

Background: Intracranial aneurysm (IA) is a critical cerebrovascular condition, and nicotine exposure is a known risk factor. This study delves into the toxicological mechanisms of nicotine in IA, aiming to identify key biomarkers and therapeutic targets.

Methods: Gene Set Variation Analysis (GSVA), Weighted Gene Co-Expression Network Analysis (WGCNA), and enrichment analyses were conducted on differentially expressed genes (DEGs) from the GSE122897 dataset. Additionally, nicotine-related targets were identified using CTD, SwissTargetPrediction, and Super-PRED databases. Integrative machine learning approaches, such as Random Forest (RF) and Support Vector Machine (SVM), were employed to pinpoint key toxicity targets. Molecular docking and immune cell infiltration analyses were also performed.

Results: DEGs in IA showed significant alterations in metabolic, secretory, signaling, and homeostatic pathways. Several immune and metabolic response pathways were notably disrupted. WGCNA identified 1127 DEGs with 37 overlapping toxic targets between IA and nicotine. ssGSEA revealed substantial upregulation in immune response and inflammation-related processes. Integrative analyses highlighted TGFB1, MCL1, and CDKN1A as core toxicity targets, confirmed via molecular docking studies. Immune cell infiltration analysis indicated significant correlations between these core targets and various immune cell populations.

Conclusion: This study uncovers significant disruptions in metabolic and immune pathways in IA under nicotine influence, identifying TGFB1, MCL1, and CDKN1A as critical biomarkers. These findings offer a deeper understanding of IA's molecular mechanisms and potential therapeutic targets for nicotine-related toxicity.