Intracranial aneurysm (IA) rupture causes severe brain hemorrhage with high mortality, yet its molecular drivers remain unclear and better risk prediction is urgently needed. Using transcriptomics, single-cell analysis, and genetic data, we investigated the role of N7-methylguanosine (m7G) RNA modification in IA. We identified distinct m7G modification patterns, validated their methylation features in patient samples, and incorporated these patterns into a machine learning-based rupture prediction model. The presence and characteristics of m7G patterns significantly improved model performance, achieving high predictive accuracy across three independent cohorts (AUC 0.91–0.95). Genetic analyses further identified three causal m7G-related genes (NSUN2, IFIT5, SNUPN), and laboratory experiments confirmed their altered expression and methylation in ruptured aneurysms. Overall, our findings demonstrate that m7G modifications play a key role in IA rupture. The validated prediction model offers strong clinical potential for rupture risk assessment, and the identified genes represent promising therapeutic targets.
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