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Mechanistic deconvolution reveals DHODH as the key target of the KDM4 inhibitor QC6352 [RNA-Seq]

GSE292934 Homo sapiens (human) RNA-seq (gene expression) 14 samples Submitted 2026/03/31 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 14 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
14 samples: 5 conditions, mostly in triplicateGroups 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)
The small molecule QC6352, developed as an inhibitor of the histone demethylase KDM4, exhibits potent anti-cancer activity, and its derivative, zavondemstat, is currently being evaluated in cancer clinical trials. Here, we demonstrate that the anti-proliferative efficacy of QC6352 in glioblastoma models is independent of KDM4 inhibition.
Read the full summary ↓
Transcriptomic analysis revealed that QC6352's gene expression signature in glioblastoma cells closely resembles that of drugs targeting dihydroorotate dehydrogenase (DHODH). Biochemical assays, protein crystallography, metabolomic analysis, and uridine rescue experiments confirmed that QC6352 inhibits DHODH and thereby disrupts the de novo pyrimidine biosynthesis pathway. Furthermore, we show that the therapeutic efficacy of zavondemstat is primarily driven by DHODH inhibition, rather than KDM4 inhibition. Lastly, by uncovering the mechanism of action of QC6352, we identify PRMT5 inhibitors as synergistic partners for DHODH inhibitors, providing the basis for maximising the therapeutic impact of DHODH inhibitors in cancer treatments.
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 GSE292934_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 PRJNA1242181 and SRA study SRP573188. Searching any of these in the dataset finder brings you back here.

Study design
5 conditions, mostly in triplicate
UT_24hr_n ×3 QC_24hr_n ×3 UT_72_120hr_n ×3 QC_120hr_n ×3 QC_72hr_n ×2

Supports a between-group comparison across 14 samples.

How we read this: 5 replicated groups read from 14 sample titles; they account for 14 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
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