Research into the molecular mechanisms underlying central nervous system (CNS) tumors has revealed significant heterogeneity and distinct molecular profiles that influence prognosis and treatment strategies. A comprehensive multi-omic analysis of primary central nervous system lymphoma (PCNSL) identified four prognostically significant clusters, highlighting the importance of molecular classification in understanding tumor behavior and patient outcomes (ref: Hernández-Verdin doi.org/10.1016/j.annonc.2022.11.002/). Similarly, a study on sinonasal tumors utilized machine learning algorithms based on DNA methylation patterns to classify tumors into four distinct molecular classes, challenging the traditional view of sinonasal undifferentiated carcinomas (SNUCs) and suggesting a more nuanced understanding of their biology (ref: Jurmeister doi.org/10.1038/s41467-022-34815-3/). In pediatric CNS tumors, the identification of a novel tumor type characterized by PLAGL1 and PLAGL2 amplifications underscores the need for tailored diagnostic and therapeutic approaches, particularly given its association with specific age groups and survival outcomes (ref: Keck doi.org/10.1007/s00401-022-02516-2/). Furthermore, targeted molecular analysis of adult tumors diagnosed as cerebellar glioblastomas revealed distinct subgroups associated with varying prognoses, emphasizing the role of molecular alterations in guiding treatment decisions (ref: Picart doi.org/10.1097/PAS.0000000000001996/). Lastly, the exploration of molecular mechanisms in temporal lobe epilepsy has provided insights into the pathophysiological underpinnings of memory dysfunction, linking specific brain regions to episodic memory impairment (ref: Busch doi.org/10.1093/braincomms/).