ArticleNeuro-oncology2026
Integrated transcriptomic landscape of medulloblastoma and ependymoma reveals novel tumor subtype-specific biology.
Article in Neuro-oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- FBXW7 induces apoptosis of nucleus pulposus cells to mediate intervertebral disc degeneration by regulating JNK expression.Journal of orthopaedic surgery and research · 2026Article
- A brief history of ependymoma.Neuro-oncology · 2026Review
- Beyond Histology: A Unified Transcriptomic Atlas Defines Lung Cancer Biologic States and Subtypes.bioRxiv : the preprint server for biology · 2026Article
- Elucidating Neurodevelopmental Trajectories in Cancer with Topic Modeling: Revealing Persistent External Granule Layer Lineages in Medulloblastoma.bioRxiv : the preprint server for biology · 2025Article
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12 authors.
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Abstract
backgroundMedulloblastoma and ependymoma are common pediatric central nervous system tumors with significant molecular and clinical heterogeneity. While molecular subgrouping has enabled classification into molecular subtypes, the extent of heterogeneity within these subgroups remains poorly defined.
methodsWe collected bulk RNA sequencing data from 888 medulloblastoma and 370 ependymoma tumors to establish a comprehensive reference landscape. After rigorous batch effect correction, normalization, and dimensionality reduction, we generated a unified landscape to explore gene expression, signaling pathways, RNA fusions, and copy number variations.
resultsOur transcriptional analysis revealed distinct clustering patterns, including two primary ependymoma compartments, EPN-E1 and EPN-E2, each with specific RNA fusions and molecular signatures. In medulloblastoma, we observed precise stratification of Group 3/4 tumors by subtype and in Sonic Hedgehog (SHH) tumors by patient age. We also identified subtype-specific pathways and gene fusions, enriched in each group.
conclusionsThis transcriptomic landscape serves as a resource for biomarker discovery, diagnostic refinement, and prediction of tumor biology and outcome. By enabling projection of new patients' bulk RNA-seq data onto the reference map using nearest neighbor analysis, the framework supports accurate subtype classification. The landscape is publicly available via Oncoscape, an interactive platform for global exploration and application.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.