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Clear-Cell Renal Cell Carcinoma Molecular Subtypes Differ by African and European Genetic Similarity

GSE289907 Homo sapiens (human) RNA-seq (gene expression) 102 samples Submitted 2025/03/14 Platform GPL20795
At a glance

RNA-seq (gene expression) · human · 102 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
102 samples: 102 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
Clear-Cell Renal Cell Carcinoma Molecular Subtypes Differ by African and European Genetic SimilarityCancer research communications · 2025
Summary (written by the submitters)
Self-reported Black (B) individuals remain underrepresented in molecular studies of clear cell renal cell carcinoma (ccRCC) relative to White (W) individuals. We performed whole-exome and transcriptome sequencing on paired tumor and normal samples from 59 matched B and W patients undergoing nephrectomy for localized ccRCC, comparing molecular differences by estimated genetic similarity to African (AFR) and European (EUR) 1000 Genomes groups.
Read the full summary ↓
We validated our findings with a propensity-matched subset of TCGA, yielding a final cohort of 254 patients (79 AFR, 175 EUR) with similar baseline clinical variables. Significant differences emerged in VHL mutation frequency (AFR: 23.4%, EUR: 57.5%; FDR = 0.0029) and chromosome 3p deletions (AFR: 59.2%, EUR: 82.6%; FDR = 0.086). Transcriptomic analyses identified 34 genes associated with genetic similarity, and gene set enrichment revealed inflammatory (interferon gamma/alpha, allograft rejection), proliferative (E2F targets, G2M checkpoint), and metabolic (bile acid, fatty acid, glycolysis, MTORC1, peroxisome) pathway enrichment in EUR. We also observed differences in ccRCC molecular subtype distribution, with “Proliferative” and “Angio/Stromal” subtypes more common in AFR (p = 0.018). Importantly, differential subtype membership explained most group-level differences. These results link EUR and AFR genetic similarity to distinct ccRCC molecular subtypes, underscoring the importance of molecular classifiers in disease stratification and the need to include diverse populations in molecular studies to improve our understanding and treatment of ccRCC.
Published in
Clear-Cell Renal Cell Carcinoma Molecular Subtypes Differ by African and European Genetic Similarity
Elias R, Nirschl T, Rezaee M et al. · Cancer research communications 2025 · PMID 40131247 · doi:10.1158/2767-9764.CRC-24-0624
This dataset
Open GSE289907 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 GSE289907_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 PRJNA1225288. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40131247) ↗
Study design
102 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 58 samples read as cases, 44 as controls.

How we read this: Read from 102 sample titles: 102 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

+ 62 more — browse all 102 samples with per-sample file links →

More Kidney 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.