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Multimodal integration of transcriptomics, proteomics and radiomics improves prediction of recurrence in patients with IDH-mutant Glioma

GSE226321 Homo sapiens (human) RNA-seq (gene expression) 59 samples Submitted 2025/04/01 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 59 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)
Topic
Lower-grade gliomaThis series is part of our curated briefing on it.
Samples
59 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
A strategy for multimodal integration of transcriptomics, proteomics, and radiomics data for the prediction of recurrence in patients with IDH-mutant gliomasInternational journal of cancer · 2025
Summary (written by the submitters)
Isocitrate dehydrogenase mutant gliomas remain lethal brain cancers which impair quality of life in young adults. These tumors are molecularly and cellularly heterogeneous and have a wide range of survival prognoses. Consequently, the identification of pa Here, we analyzed imaging, transcriptomic, and proteomic profiles using machine learning to 1) describe the biological characterization of subtypes of IDH-mutant gliomas categorized by PET and histology, 2) reinforce the integration of PET metrics in the
Published in
A strategy for multimodal integration of transcriptomics, proteomics, and radiomics data for the prediction of recurrence in patients with IDH-mutant gliomas
Chouleur T, Etchegaray C, Villain L et al. · International journal of cancer 2025 · PMID 40214613 · doi:10.1002/ijc.35441
This dataset
Open GSE226321 in finder → Find more human RNA-seq datasets → Lower-grade glioma landscape →
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 GSE226321_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 PRJNA939698 and SRA study SRP430966. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40214613) ↗
Study design
A cohort of 59 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 Brain_patient_114 … Brain_patient_81 and carry no group information.

Samples in this study

+ 19 more — browse all 59 samples with per-sample file links →

More Lower-grade glioma datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.