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Gene expression analysis of non-clear-cell renal cell carcinoma and clear-cell renal cell carcinoma kidney cancers

GSE312695 Homo sapiens (human) RNA-seq (gene expression) 140 samples Submitted 2025/12/10 Platform GPL24676Platform GPL28038
At a glance

RNA-seq (gene expression) · human · 140 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
140 samples: 140 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
Non-Clear Cell Renal Cell Carcinoma Has an Immune-Cold Phenotype That Affects Response to Immune Checkpoint InhibitorsMolecular cancer research : MCR · 2026
Summary (written by the submitters)
Data regarding the clinical outcomes of immune checkpoint inhibitor (ICI) therapy and profiles of the tumor immune microenvironment (TIME) in non-clear-cell renal cell carcinoma (nccRCC) remain limited. Here, we retrospectively compared the effectiveness profiles of first-line ICI combination therapy between patients with clear-cell RCC (ccRCC) and those with nccRCC.
Published in
Non-Clear Cell Renal Cell Carcinoma Has an Immune-Cold Phenotype That Affects Response to Immune Checkpoint Inhibitors
Ishihara H, Liu YY, Yamashita R et al. · Molecular cancer research : MCR 2026 · PMID 41591388 · doi:10.1158/1541-7786.MCR-25-0939
This dataset
Open GSE312695 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 GSE312695_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 PRJNA1375726 and SRA study SRP651776. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41591388) ↗
Study design
140 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 140 sample titles: 140 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

+ 100 more — browse all 140 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.