← BioTransfer GEO Dataset Finder
GEO series

Nuclear speckles regulate functional programs in cancer (RNA SART1, DDX39A, SARNP, FIBP, TMEM259 kd)

GSE269570 Homo sapiens (human) RNA-seq (gene expression) 26 samples Submitted 2024/09/03 Platform GPL21697
At a glance

RNA-seq (gene expression) · human · 26 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
Kidney cancerThis series is part of our curated briefing on it.
Samples
26 samples: 26 samples, each with a distinct title — no replicated groupsGroups 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
Nuclear speckles regulate functional programs in cancerNature cell biology · 2025
Summary (written by the submitters)
Nuclear speckles are dynamic nuclear bodies characterized by high local concentrations of RNA binding proteins and specific non-coding RNAs. Although the contents of speckles suggest multifaceted roles in regulating chromatin dynamics and gene expression, the overarching biological function(s) of nuclear speckles remain enigmatic. In this study, we investigate speckle compositional variation in human cancer, finding two main speckle compositional states based on RNA expression of speckle-resident proteins.
Read the full summary ↓
One cancer speckle state was more similar to normal adjacent tissues, while the other was dissimilar from normal tissue, and thus considered an aberrant cancer speckle state. We link the aberrant speckle state to altered speckle positioning within the nucleus, to elevation of the TREX RNA export complex, and to worse patient outcomes in clear cell renal cell carcinoma (ccRCC). ccRCC is typified by hyperactivation of the HIF-2a transcription factor, and we demonstrate that HIF-2a drives physical association of a select subset of its target genes with nuclear speckles depending on HIF-2a's two speckle targeting motifs (STMs) defined in this study. STMs are highly enriched among transcription factors, suggesting that DNA-speckle targeting may be a general mechanism of gene regulation and providing a resource of candidate speckle-targeting factors. Via integration of tissue culture functional studies with tumor genomic and imaging analysis, we show that HIF-2a gene regulatory programs are impacted by speckle compositional state and by abrogation of speckle targeting abilities of HIF-2a. These findings suggest that, in ccRCC, a key biological function of nuclear speckles is to modulate expression of a specific subset of HIF-2a-regulated target genes that, in turn, influence patient outcomes. Beyond ccRCC, tumor speckle compositional states broadly correlate with altered functional pathways and expression of speckle-associated gene neighborhoods, exposing a general link between nuclear speckles and gene expression dysregulation in human cancer.
Published in
Nuclear speckles regulate functional programs in cancer
Alexander KA, Yu R, Skuli N et al. · Nature cell biology 2025 · PMID 39747580 · doi:10.1038/s41556-024-01570-0
This dataset
Open GSE269570 in finder → Find more human RNA-seq datasets → Kidney 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 GSE269570_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 PRJNA1122533 and SRA study SRP513193. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39747580) ↗
Study design
26 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 26 sample titles: 26 distinct titles with little repetition. 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
More Kidney cancer datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.