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KLF5 interacts with RUVBL1/2 to activate classical and basal lineage identities in PDAC [RNA-seq]

GSE295349 Homo sapiens (human) RNA-seq (gene expression) 183 samples Submitted 2025/10/08 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 183 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
Pancreatic cancerThis series is part of our curated briefing on it.
Samples
183 samples: 53 conditions, mostly with about 4 replicates eachGroups 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.
Paper
KLF5 enables dichotomous lineage programs in pancreatic cancer via the AAA+ ATPase coactivators RUVBL1 and RUVBL2Nature communications · 2025
Summary (written by the submitters)
We identify KLF5 as a lineage master regulator and a dependency in classical and basal subtypes of human PDAC. We integrate proteomic and genetic screens to reveal RUVBL1 and RUVBL2 as transcriptional coactivators of KLF5 in PDAC. We use biochemical and epigenomic methods to validate that KLF5-RUVBL1/2 complexes directly activate lineage identity genes in PDAC.
Published in
KLF5 enables dichotomous lineage programs in pancreatic cancer via the AAA+ ATPase coactivators RUVBL1 and RUVBL2
Cunniff PJ, Sivetz N, Skopelitis D et al. · Nature communications 2025 · PMID 41241675 · doi:10.1038/s41467-025-66007-0
This dataset
Open GSE295349 in finder → Find more human RNA-seq datasets → Pancreatic cancer 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 GSE295349_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 PRJNA1254249 and SRA study SRP580565. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41241675) ↗
Study design
53 conditions, mostly with about 4 replicates each
AsPC1_sgKLF5_rep1 ×6 AsPC1_sgCHD4 ×4 AsPC1_sgRosa_for_sgCHD4 ×4 BxPC3_sgKLF5_rep1 ×4 AsPC1_sgRosa_rep2 ×4 AsPC1_sgRUVBL1_rep2 ×4 AsPC1_sgSERPINB5_rep2 ×4 T3M4_sgKLF5_rep1 ×4 +51 more

Supports a between-group comparison across 177 samples.

How we read this: 53 replicated groups read from 183 sample titles; they account for 177 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

+ 143 more — browse all 183 samples with per-sample file links →

More Pancreatic cancer 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.