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Meningioma transcriptomic landscape demonstrates novel subtypes with regional associated biology and patient outcome

GSE252291 Homo sapiens (human) RNA-seq (gene expression) 279 samples Submitted 2024/04/26 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 279 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
279 samples
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Meningioma transcriptomic landscape demonstrates novel subtypes with regional associated biology and patient outcomeCell genomics · 2024
Summary (written by the submitters)
Meningiomas are the most common primary intracranial tumors in humans. While most of these tumors are benign, some are malignant, rapidly recur after multimodal treatment with surgery and radiotherapy, and can ultimately be fatal. The current WHO grade system does not always identify high risk meningiomas, therefore better characterizations of the biology of aggressive tumors are needed.
Read the full summary ↓
In order to address these challenges, we combined 13 bulk RNA-Seq datasets, corrected for batch effects, and applied Uniform Manifold Approximation and Projection (UMAP) to create a reference landscape of ~1300 meningioma tumors. Our analyses revealed multiple distinct meningioma subtypes with specific biological signatures. Clinical metadata, mutations, copy number alterations, and gene-fusion data effectively correlated with regions of the UMAP. Notably, regional distribution of time to recurrence identified major clusters as well as intra-cluster differences of meningiomas with varying patient outcomes. The most aggressive subtype, characterized by an enrichment of higher WHO grades, frequent tumor recurrences, and shorter time to recurrence, exhibited elevated proliferation rates and RNA expression resembling muscle development. To facilitate clinical applications, we developed a cross-validated nearest-neighbors-based algorithm that accurately maps new patients onto this UMAP landscape. Our study highlights the utility of transcriptomic analysis in discerning meningioma heterogeneity as well as successful combination of multiple datasets from various sources. We provide a valuable tool for understanding the disease, predicting tumor biology and patient prognosis. This resource is accessible via the open source, interactive online tool Oncoscape, where the scientific community can explore the landscape and mine clinical and genomic metadata.
Published in
Meningioma transcriptomic landscape demonstrates novel subtypes with regional associated biology and patient outcome
Thirimanne HN, Almiron-Bonnin D, Nuechterlein N et al. · Cell genomics 2024 · PMID 38788713 · doi:10.1016/j.xgen.2024.100566
This dataset
Open GSE252291 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE252291_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1059263 and SRA study SRP480733. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38788713) ↗
Study design
A cohort of 279 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run MNG170 … MNG969 and carry no group information.

Samples in this study

+ 239 more — browse all 279 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.