%0 Journal Article
%A Okonechnikov, Konstantin
%A Ghasemi, David R
%A Schrimpf, Daniel
%A Tonn, Svenja
%A Mynarek, Martin
%A Koster, Jan
%A Milde, Till
%A Zheng, Tuyu
%A Sievers, Philipp
%A Sahm, Felix
%A Jones, David T W
%A von Deimling, Andreas
%A Pfister, Stefan M
%A Kool, Marcel
%A Pajtler, Kristian W
%A Korshunov, Andrey
%T Biglycan-driven risk stratification in ZFTA-RELA fusion supratentorial ependymomas through transcriptome profiling.
%J Acta Neuropathologica Communications
%V 13
%N 1
%@ 2051-5960
%C London
%I Biomed Central
%M DKFZ-2025-00064
%P 4
%D 2025
%Z #EA:B062#LA:B300#
%X Recent genomic studies have allowed the subdivision of intracranial ependymomas into molecularly distinct groups with highly specific clinical features and outcomes. The majority of supratentorial ependymomas (ST-EPN) harbor ZFTA-RELA fusions which were designated, in general, as an intermediate risk tumor variant. However, molecular prognosticators within ST-EPN ZFTA-RELA have not been determined yet. Here, we performed methylation-based DNA profiling and transcriptome RNA sequencing analysis of 80 ST-EPN ZFTA-RELA investigating the clinical significance of various molecular patterns. The principal types of ZFTA-RELA fusions, based on breakpoint location, demonstrated no significant correlations with clinical outcomes. Multigene analysis disclosed 1892 survival-associated genes, and a metagene set of 100 genes subdivided ST-EPN ZFTA-RELA into favorable and unfavorable transcriptome subtypes composed of different cell subpopulations as detected by deconvolution analysis. BGN (biglycan) was identified as the top-ranked survival-associated gene and high BGN expression levels were associated with poor survival (Hazard Ratio 17.85 for PFS and 45.48 for OS; log-rank; p-value < 0.01). Furthermore, BGN immunopositivity was identified as a strong prognostic indicator of poor survival in ST-EPN, and this finding was confirmed in an independent validation set of 56 samples. Our results indicate that integrating BGN expression (at mRNA and/or protein level) into risk stratification models may improve ST-EPN ZFTA-RELA outcome prediction. Therefore, gene and/or protein expression analyses for this molecular marker could be adopted for ST-EPN ZFTA-RELA prognostication and may help assign patients to optimal therapies in prospective clinical trials.
%K Humans
%K Ependymoma: genetics
%K Ependymoma: metabolism
%K Ependymoma: pathology
%K Supratentorial Neoplasms: genetics
%K Supratentorial Neoplasms: pathology
%K Supratentorial Neoplasms: metabolism
%K Male
%K Female
%K Gene Expression Profiling: methods
%K Transcription Factor RelA: genetics
%K Transcription Factor RelA: metabolism
%K Adult
%K Middle Aged
%K Adolescent
%K Child
%K Young Adult
%K Child, Preschool
%K Aged
%K Prognosis
%K Risk Assessment: methods
%K Oncogene Proteins, Fusion: genetics
%K Oncogene Proteins, Fusion: metabolism
%K BGN (Other)
%K ZFTA-RELA fusion (Other)
%K Ependymoma (Other)
%K Expression (Other)
%K Prognosis (Other)
%K Transcription Factor RelA (NLM Chemicals)
%K RELA protein, human (NLM Chemicals)
%K Oncogene Proteins, Fusion (NLM Chemicals)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:39762990
%R 10.1186/s40478-024-01921-w
%U https://inrepo02.dkfz.de/record/296134