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Conserved patterns of transcriptional dysregulation, heterogeneity and cell states in clear cell kidney cancer [scRNA-seq]

GSE269826 Homo sapiens (human) Single-cell RNA-seq 79 samples Submitted 2024/12/18 Platform GPL30173Platform GPL24676
At a glance

Single-cell RNA-seq · human · 79 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Topic
Kidney cancerThis series is part of our curated briefing on it.
Samples
79 samples: 71 distinct titles, 1 of them repeatedGroups 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
Conserved patterns of transcriptional dysregulation, heterogeneity, and cell states in clear cell kidney cancerCell reports · 2025
Summary (written by the submitters)
Clear cell kidney cancers (ccRCC) stem from proximal tubule cells in which the VHL gene has been disrupted, causing activation of HIF transcription factors. Although this is essential for cancer initiation, it not sufficient for development of aggressive tumors. To better understand transcriptional programs underpinning ccRCC progression, we perform single cell transcriptomics on 75 kidney tumor and patient-matched normal kidney biopsies.
Read the full summary ↓
We describe tumor dysregulated transcriptional programs that are conserved across patients, deconvoluted by cell type. Leveraging recurrent intratumor heterogeneity in chromosome 14q loss, a metastasis-associated copy number alteration, we describe the ensuing transcriptional programs to reveal potential mechanisms priming cells for metastasis. Lastly, we describe co-existing cancer cell states consistently found in all patients, underpinned by distinct transcriptional regulators and whose transcriptional signatures are prognostic. These cancer cell states reflect heterogeneity found in the normal proximal tubule epithelium, strongly indicating that they arise from lineage plasticity.
Published in
Conserved patterns of transcriptional dysregulation, heterogeneity, and cell states in clear cell kidney cancer
Lombardi O, Li R, Jabbar F et al. · Cell reports 2025 · PMID 39792555 · doi:10.1016/j.celrep.2024.115169
This dataset
Open GSE269826 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 GSE269826_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1123757. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39792555) ↗
Study design
71 distinct titles, 1 of them repeated
N ×9

How we read this: Read from 79 sample titles: 71 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

+ 39 more — browse all 79 samples with per-sample file links →

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.