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Comprehensive Genomic Profiling of Renal Cancer Cases

GSE294575 Homo sapiens (human) RNA-seq (gene expression) 121 samples Submitted 2025/05/06 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 121 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
121 samples: 116 × KR_T vs 3 × KUR_TGroups 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
Evaluation of the diagnostic concordance of FDA-approved PD-L1 assays in clear cell renal cell carcinomaScientific reports · 2025
Summary (written by the submitters)
Comprehensive Genomic Profiling (CGP) is a technique that comprehensively examines a wide range of cancer-related genetic alterations—such as mutations, copy number variations, and gene fusions—from a single specimen. In the field of renal cancer (primarily renal cell carcinoma), where treatment options like molecularly targeted therapies and immune checkpoint inhibitors continue to expand, there is growing interest in understanding each patient’s mutational profile to enable more personalized treatment strategies.
Published in
Evaluation of the diagnostic concordance of FDA-approved PD-L1 assays in clear cell renal cell carcinoma
Koh Y, Kato T, Yamamichi G et al. · Scientific reports 2025 · PMID 40593071 · doi:10.1038/s41598-025-05697-4
This dataset
Open GSE294575 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 GSE294575_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 PRJNA1250523 and SRA study SRP578538. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40593071) ↗
Study design
116 × KR_T vs 3 × KUR_T

Supports a between-group comparison across 119 samples.

How we read this: 2 replicated groups read from 121 sample titles; they account for 119 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

+ 81 more — browse all 121 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.