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The novel RNA polymerase I transcription inhibitor PMR-116 exploits a critical therapeutic vulnerability in a broad-spectrum of high MYC malignancies

GSE302078 Homo sapiens (human) RNA-seq (gene expression) 56 samples Submitted 2025/07/08 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 56 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
56 samples: 16 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)
Ribosome biogenesis (RiBi) is a key determinant of cell growth and proliferation and is highly elevated in cancer due to the activation by oncogenes such as MYC. First-generation RiBi inhibitor CX-5461, while demonstrating clinical potential for cancer treatment, also induces DNA damage through off-target inhibition of TOP2⍺ and potentially other mechanisms, bringing into question RiBi as a target for cancer therapy.
Read the full summary ↓
In this study, we test second-generation RiBi inhibitor, PMR-116. PMR-116 exhibits improved drug-like properties compared to first-generation RiBi inhibitors and has robust anti-tumour activity in the absence of global DNA damage signalling in a broad range of pre-clinical models of haematologic and solid cancers, particularly in malignancies where MYC is either the driver of disease or is elevated. Thus, our work demonstrates that RiBi is a genuine target for cancer therapy and highlights the potential to exploit a critical therapeutic vulnerability in high-MYC human cancers with dismal therapeutic outcomes.
This dataset
Open GSE302078 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 GSE302078_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 PRJNA1288473 and SRA study SRP599205. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
16 conditions, mostly in triplicate
KG-1 3hrs CX 1uM n ×4 KG-1 3hrs PMR Veh n ×4 KG-1 3hrs PMR 1uM n ×4 KG-1 12hrs CX Veh n ×4 KG-1 12hrs CX 1uM n ×4 KG-1 12hrs PMR Veh n ×4 KG-1 12hrs PMR 1uM n ×4 KG-1 3hrs CX Veh n ×3 +9 more

Supports a between-group comparison across 55 samples.

How we read this: 16 replicated groups read from 56 sample titles; they account for 55 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

+ 16 more — browse all 56 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.