DNA methylation regulator-mediated modification patterns and risk of intracranial aneurysm: a multi-omics and epigenome-wide association study integrating machine learning, Mendelian randomization, eQTL and mQTL data

DNA甲基化调节因子介导的修饰模式与颅内动脉瘤风险:一项整合机器学习、孟德尔随机化、eQTL和mQTL数据的多组学和全表观基因组关联研究

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Abstract

BACKGROUND: Intracranial aneurysms (IAs) pose a significant and intricate challenge. Elucidating the interplay between DNA methylation and IA pathogenesis is paramount to identify potential biomarkers and therapeutic interventions. METHODS: We employed a comprehensive bioinformatics investigation of DNA methylation in IA, utilizing a transcriptomics-based methodology that encompassed 100 machine learning algorithms, genome-wide association studies (GWAS), Mendelian randomization (MR), and summary-data-based Mendelian randomization (SMR). Our sophisticated analytical strategy allowed for a systematic assessment of differentially methylated genes and their implications on the onset, progression, and rupture of IA. RESULTS: We identified DNA methylation-related genes (MRGs) and associated molecular pathways, and the MR and SMR analyses provided evidence for potential causal links between the observed DNA methylation events and IA predisposition. CONCLUSION: These insights not only augment our understanding of the molecular underpinnings of IA but also underscore potential novel biomarkers and therapeutic avenues. Although our study faces inherent limitations and hurdles, it represents a groundbreaking initiative in deciphering the intricate relationship between genetic, epigenetic, and environmental factors implicated in IA pathogenesis.

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