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Transcriptional changes mediated by the HDACi belinostat in IDH-mutant glioma cultures with YAP and TAZ overexpression

GSE308636 Homo sapiens (human) RNA-seq (gene expression) 36 samples Submitted 2025/09/19 Platform GPL28038
At a glance

RNA-seq (gene expression) · human · 36 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
36 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.
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 greater sensitivity to HDACi only in glioma, not in IDHmut chondrosarcoma or cholangiocarcinoma. Two mediators of HDACi resistance, YAP and TAZ, were methylated and suppressed in IDHmut gliomas, but not in other IDHmut cancers. Inducing YAP or TAZ expression in IDHmut gliomas conferred resistance to HDACi. Here, we report the transcriptional changes mediated by the HDACi belinostat in IDHmut glioma with and without YAP or TAZ expression.
This dataset
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.

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 GSE308636_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1331761 and SRA study SRP622588. Searching any of these in the dataset finder brings you back here.

Samples in this study

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