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Association between blood parameters, whole-blood transcriptomic profiles, and immune-related adverse events in metastatic renal cell carcinoma during nivolumab plus ipilimumab

GSE319496 Homo sapiens (human) RNA-seq (gene expression) 51 samples Submitted 2026/02/14 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 51 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
51 samples: 51 × Pretreatment whole blood RNA-seq, SampleGroups 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
Blood parameters and whole-blood transcriptomics associated with immune-related adverse events in metastatic renal cell carcinoma during nivolumab plus ipilimumabScientific reports · 2026
Summary (written by the submitters)
Pretreatment immune profiles associated with immune-related adverse events (irAEs) may inform individualized management strategies, including enhanced monitoring and early toxicity intervention, in patients with metastatic renal cell carcinoma (mRCC) treated with nivolumab plus ipilimumab. In this cohort (n = 51), we analyzed pretreatment peripheral blood parameters and whole-blood transcriptomic profiles.
Read the full summary ↓
Higher lymphocyte and monocyte counts (odds ratios [ORs] 14.36 and 9.90, respectively) were significantly associated with an increased risk of irAEs, whereas higher neutrophil counts and C-reactive protein levels (ORs 0.08 and 0.27, respectively) were associated with a decreased risk. To characterize immune pathways associated with irAE development, we performed whole-blood transcriptomic analyses. Gene set enrichment analysis revealed upregulation of lymphocyte- and humoral immunity-related pathways and downregulation of neutrophil- and inflammatory-related pathways in patients who developed irAEs. CIBERSORTx demonstrated reduced neutrophil fractions and increased proportions of CD8⁺ T cells and activated memory CD4⁺ T cells, indicating an adaptive immune-dominant profile. Exploratory analysis identified five candidate genes (ANAPC1, CDK4, MCM6, GRAP2, and BST2) that may contribute to the immune features associated with irAE development. Collectively, these findings suggest that irAE development is associated with a distinct systemic immune profile characterized by enhanced lymphocyte-related and reduced neutrophil-related immune activity.
Published in
Blood parameters and whole-blood transcriptomics associated with immune-related adverse events in metastatic renal cell carcinoma during nivolumab plus ipilimumab
Kinase S, Nagumo Y, Isoda B et al. · Scientific reports 2026 · PMID 42026114 · doi:10.1038/s41598-026-46960-6
This dataset
Open GSE319496 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 GSE319496_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 PRJNA1423615. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42026114) ↗
Study design
51 × Pretreatment whole blood RNA-seq, Sample

Supports a between-group comparison across 51 samples.

How we read this: 1 replicated groups read from 51 sample titles; they account for 51 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

+ 11 more — browse all 51 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.