Recent studies have significantly advanced our understanding of the molecular mechanisms underlying brain tumors, particularly gliomas and medulloblastomas. A multi-cohort study highlighted the prevalence and implications of primary mismatch repair deficiency (MMRD) in gliomas among children and young adults, revealing unique biological characteristics that could inform therapeutic strategies (ref: Negm doi.org/10.1016/S1470-2045(24)00640-5/). Moreover, the application of nanopore sequencing in medulloblastoma classification demonstrated its potential to provide clinically relevant methylation and copy number profiles, outperforming traditional methods (ref: Filser doi.org/10.1093/neuonc/). Machine learning approaches have also been employed to stratify glioblastoma patients into prognostic subgroups based on clinical data and molecular measures, enhancing personalized treatment plans (ref: Akbari doi.org/10.1093/neuonc/). In the context of recurrent glioblastoma, neoadjuvant anti-PD1 immunotherapy was shown to correlate with a unique cell cycle gene signature that serves as a positive risk factor for survival, emphasizing the role of molecular profiling in treatment response (ref: McFaline-Figueroa doi.org/10.1038/s41467-024-54326-7/). Additionally, a comprehensive analysis of epileptogenic brain lesions revealed associations with specific genetic factors, such as DYRK1A and EGFR, which may guide future therapeutic interventions (ref: Boßelmann doi.org/10.1038/s41467-024-54911-w/). Finally, the use of amide proton transfer-weighted MRI has emerged as a promising imaging biomarker for differentiating glioma recurrence from pseudoprogression, showcasing the integration of advanced imaging techniques in neuro-oncology (ref: Karimian-Jazi doi.org/10.1097/RLI.0000000000001145/).