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Stratification of renal cell carcinoma patients by abundance of sarcomatoid features reveals differences in survival and underlying pathobiology

GSE261172 Homo sapiens (human) RNA-seq (gene expression) 45 samples Submitted 2025/03/15 Platform GPL11154
At a glance

RNA-seq (gene expression) · human · 45 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
45 samples: 24 × SarcomatoidHigh vs 20 × SarcomatoidLowGroups 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.
Summary (written by the submitters)
Sarcomatoid renal cell carcinoma (sRCC) is histologically heterogenous, with variable sarcomatoid amounts intermixed within epithelial carcinoma, however current classification of this aggressive disease is homogenous and agnostic to sarcomatoid proportion. We investigated whether sRCC sub-classification could improve prognostic value and reveal biology underlying dedifferentiation and its clinical aggressiveness.
Read the full summary ↓
Based on intratumoral abundance of sarcomatoid features, cases were classified as sarcomatoid-high (≥10% sarcomatoid features) or sarcomatoid-low (<10% sarcomatoid features) within a consecutive sRCC nephrectomy patient cohort at a single center (n=104). Compared to sarcomatoid-low patients (n=52), sarcomatoid-high patients (n=52) had significantly shorter overall survival (median 14.5 vs. 62.9 months, p<0.001) including in multivariable analysis, and significantly shorter metastasis-free survival among clinically localized patients (median 10.7 vs. 39.0 months, p=0.043). Transcriptomic analyses of n=45 sRCC tumors revealed significant upregulation of 10 Hallmark pathways relating to cell cycle/proliferation, epithelial-to-mesenchymal transition, reactive oxidative species, and interferon-alpha signaling in sarcomatoid-high (n=24) versus sarcomatoid-low (n=21) tumors. Categorization into transcriptomic clusters revealed predominance of proliferative, inflammatory, and immune effector phenotypes within sarcomatoid-high tumors versus hypoxia/angiogenesis in sarcomatoid-low tumors. Altogether, these findings indicate prognostic value for sRCC sub-classification into high versus low sarcomatoid groups and highlight key biology underlying differences in clinical outcome.
This dataset
Open GSE261172 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 GSE261172_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 PRJNA1085688 and SRA study SRP494142. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
24 × SarcomatoidHigh vs 20 × SarcomatoidLow

Supports a between-group comparison across 44 samples.

How we read this: 2 replicated groups read from 45 sample titles; they account for 44 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

+ 5 more — browse all 45 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.