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Genetic landscape and functional exploration of kidney cancer predisposition causality in cross-ancestral populations [screen]

GSE279304 Homo sapiens (human) Single-cell RNA-seq 24 samples Submitted 2026/02/10 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 24 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Topic
Kidney cancerThis series is part of our curated briefing on it.
Samples
24 samples: 12 conditions, mostly in duplicateGroups 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
Genetic landscape and functional exploration of kidney cancer predisposition in cross-ancestral populationsNature communications · 2026
Summary (written by the submitters)
To functionally explore renal cell carcinoma (RCC) susceptibility variants, we performed CUT&Tag for H3K27ac and ATAC-seq on 769P cells to assess transcriptional activity. Subsequently, we conducted a CRISPR screen and CRISPR droplet sequencing (CROP-seq), which combines pooled CRISPR screens with single-cell RNA sequencing (scRNA-seq), to identify target genes potentially regulated by likely causal SNPs.
Read the full summary ↓
Functionally, we established a novel association between rs28684409 and the oncogene RPL4 at the complex genetic locus 15q22.31. As the next step, we performed CRISPRi on rs28684409 and conducted RNA-seq to identify differential gene expression associated with this variant.
Published in
Genetic landscape and functional exploration of kidney cancer predisposition in cross-ancestral populations
Dai H, Chu X, Du H et al. · Nature communications 2026 · PMID 42000752 · doi:10.1038/s41467-026-71785-2
This dataset
Open GSE279304 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 GSE279304_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 PRJNA1171825 and SRA study SRP537970. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42000752) ↗
Study design
12 conditions, mostly in duplicate
CRISPR screening, 786-O,CRISPRi, Day0, ×2 CRISPR screening, 786-O,CRISPRi, Day14, ×2 CRISPR screening, 786-O,CRISPRi, Day21, ×2 CRISPR screening, 786-O,CRISPRa, Day0, ×2 CRISPR screening, 786-O,CRISPRa, Day14, ×2 CRISPR screening, 786-O,CRISPRa, Day21, ×2 CRISPR screening, Caki-1,CRISPRi, Day0, ×2 CRISPR screening, Caki-1,CRISPRi, Day14, ×2 +4 more

Supports a between-group comparison across 24 samples.

How we read this: 12 replicated groups read from 24 sample titles; they account for 24 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
More Kidney cancer datasets

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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.