Abstract
Meningiomas are the most common primary brain tumors, accounting for 40% of all central nervous system neoplasms. While usually benign, these tumors can vary in aggressiveness. Traditional classification and grading systems, which primarily rely on histopathological features, are not always reliable in capturing tumor behavior and predicting patient outcomes. In contrast, modern systems-based on factors such as copy number alterations, DNA methylation, and gene expression-offer a more accurate framework for identifying distinct biological signatures and aggressive subtypes, as well as for predicting recurrence. Transcriptomic profiling using bulk whole-genome RNA sequencing (RNA-seq), which provides insights into alternative splicing, gene expression, fusion events, non-coding RNAs, and pathway activity, further enhances our understanding of meningioma tumorigenesis, enables the projection of new samples onto dimension-reduced reference landscapes, and helps accurately predict recurrence. As bulk RNA-seq becomes more accessible, it holds great potential for refining prognostic tools, informing personalized treatment approaches, and ultimately improving outcomes for meningioma patients.