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Molecular Profile of Interleukin-17RA Blockade by Brodalumab in Japanese Patients with Psoriasis: Results from the ESPRTI study

GSE268958 Homo sapiens (human) RNA-seq (gene expression) 163 samples Submitted 2025/12/31 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 163 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)
Samples
163 samples: 163 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
Molecular profile of interleukin-17RA blockade by brodalumab in Japanese patients with psoriasis: Results from the ESPRIT studyJournal of dermatological science · 2025
Summary (written by the submitters)
Brodalumab, an anti-interleukin-17 receptor antibody, is presumed to be effective in psoriasis due to its action on the IL-17 pathway. This study is an analysis of an exploratory intervention study (ESPRIT) conducted in Japanese patients with moderate to severe plaque psoriasis. The efficacy of brodalumab was analyzed by clinical changes and modulation of skin inflammation by changes in gene expression in the skin and protein expression in blood samples.
Published in
Molecular profile of interleukin-17RA blockade by brodalumab in Japanese patients with psoriasis: Results from the ESPRIT study
Krueger JG, Morita A, Uchida-Yamada M et al. · Journal of dermatological science 2025 · PMID 41077515 · doi:10.1016/j.jdermsci.2025.09.005
This dataset
Open GSE268958 in finder → Find more human RNA-seq datasets →
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 GSE268958_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 PRJNA1119501 and SRA study SRP511440. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41077515) ↗
Study design
163 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 163 sample titles: 163 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

+ 123 more — browse all 163 samples with per-sample file links →

Datasets on a similar topic

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.