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Transcriptional changes mediated by the HDACi panobinostat in IDH-wildtype and IDH-mutant gliomas

GSE308634 Homo sapiens (human) RNA-seq (gene expression) 54 samples Submitted 2025/09/19 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 54 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
54 samples: 14 conditions, mostly in duplicateGroups are read from the sample names, not from the paper. Check the list below.
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.
Summary (written by the submitters)
IDH1/2 mutations (IDHmut) increase methylation of DNA and histones in gliomas. IDHmut inhibitors are effective against low-grade IDHmut gliomas, but new strategies against high grade IDHmut gliomas are needed. Although histone deacetylase inhibitors (HDACi) are ineffective against IDHwt glioblastoma (GBM), their potential in IDHmut gliomas has not been extensively studied.
Read the full summary ↓
We previously established that IDHmut gliomas are more sensitive to HDACi than IDHwt GBM. Here, we show that IDHmut is associated with unique transcriptional changes mediated by the HDACi panobinostat in glioma, providing insight into the mecahnism of increased HDACi sensitivithy..
This dataset
Open GSE308634 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 GSE308634_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 PRJNA1331756 and SRA study SRP622562. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
14 conditions, mostly in duplicate
BT142_10nM_p12 ×2 BT142_Veh_p12 ×2 GBM12_10nM_8p2 ×2 GBM12_Veh_8p2 ×2 GBM43_10nM_7p4 ×2 GBM43_Veh_7p4 ×2 S0827_10nM_p10 ×2 S0827_Veh_p10 ×2 +32 more

Supports a between-group comparison across 28 samples.

How we read this: 14 replicated groups read from 54 sample titles; they account for 28 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 14 more — browse all 54 samples with per-sample file links →

More Lower-grade glioma datasets

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.