RNA splicing as a biomarker and phenotypic driver of meningioma DNA-methylation groups

• DOI: 10.1093/neuonc/noae150
• Publication Date: 2024-08-02T16:21:09Z
• Authors: Nathan K Leclair; Abrar Choudury; William C Chen; Stephen T Magill; Kathleen McCortney; Craig M Horbinski; Zhenhong Chen; Ezequiel Goldschmidt; Charlotte D Eaton; Ketan R Bulsara; Wenya Linda Bi; Akash J Patel; Felix Sahm; David Raleigh; Olga Anczukow
• Keywords: 0301 basic medicine; Medical Sciences; RNA splicing; RNA Splicing; 610; risk stratification; RNA-based therapeutics; meningioma; transcriptomics; 03 medical and health sciences; Medical Specialties; Medicine and Health Sciences; Meningeal Neoplasms; Humans; Neoplastic; Tumor; Cultured; JGM; Mental and Social Health; Neurosciences; DNA Methylation; Prognosis; Tumor Cells; Phenotype; Neurology; Gene Expression Regulation; Basic and Translational Investigations; Meningioma; Biomarkers

Abstract

Background Advances in our understanding of the molecular biology of meningiomas have led to significant gains in the ability to predict patient prognosis and tumor recurrence and to identify novel targets for therapeutic design. Specifically, classification of meningiomas based on DNA methylation has greatly improved our ability to risk stratify patients, however new questions have arisen in terms of the underlying impact these DNA-methylation signatures have on meningioma biology.

Methods This study utilizes RNA-sequencing data from 486 meningioma samples corresponding to 3 meningioma DNA-methylation groups (merlin-intact, immune-enriched, and hypermitotic), followed by in vitro experiments utilizing human meningioma cell lines.

Results We identify alterations in RNA splicing between meningioma DNA-methylation groups including individual splicing events that correlate with hypermitotic meningiomas and predict tumor recurrence and overall patient prognosis and compile a set of splicing events that can accurately predict DNA-methylation classification based on RNA-seq data. Furthermore, we validate these events using reverse transcription polymerase chain reaction (RT-PCR) in patient samples and meningioma cell lines. Additionally, we identify alterations in RNA-binding proteins and splicing factors that lie upstream of RNA splicing events, including upregulation of SRSF1 in hypermitotic meningiomas which we show drives alternative RNA splicing changes. Finally, we design splice-switching antisense oligonucleotides to target RNA splicing changes in NASP and MFF observed in hypermitotic meningiomas, providing a rationale for RNA-based therapeutic design.

Conclusions RNA splicing is an important driver of meningioma phenotypes that can be useful in prognosticating patients and as a potential exploit for therapeutic vulnerabilities.