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Assessing T-Cell Profile Shifts via IL-23 Inhibition by Guselkumab on Psoriasis.

GSE290870 Homo sapiens (human) RNA-seq (gene expression) 72 samples Submitted 2025/09/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 72 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
72 samples: 72 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
Assessing T-Cell Profile Shifts through IL-23 Inhibition by Guselkumab on PsoriasisJID innovations : skin science from molecules to population health · 2026
Summary (written by the submitters)
Anti-IL-23 antibody therapies result in improvements to the underlying immunopathology of psoriasis. However, the dynamics of the immune profile after the administration of anti-IL-23 biologics, along with their association with treatment response, remain unclear investigation. We focused on guselkumab, an anti-IL-23p19 antibody, and performed a comprehensive analysis of immune cells, serum inflammatory molecules, and transcriptomics of CD4+ T cells, aiming to identify disease-modifying drug effects in psoriasis.
Read the full summary ↓
A total of 24 biologic-naive patients were enrolled. Peripheral and skin lesional blood samples were collected at baseline and after treatment with guselkumab. We conducted FACS analysis of regulatory T cells (Tregs), resident memory T cells (TRMs), and dendritic cells, measured serum cytokine and chemokine levels, and examined gene expression changes using RNA-seq data for peripheral CD4+ T cells.
Published in
Assessing T-Cell Profile Shifts through IL-23 Inhibition by Guselkumab on Psoriasis
Kanayama Y, Watanabe O, Sakurai M et al. · JID innovations : skin science from molecules to population health 2026 · PMID 41459381 · doi:10.1016/j.xjidi.2025.100433
This dataset
Open GSE290870 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 GSE290870_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 PRJNA1230558 and SRA study SRP567365. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41459381) ↗
Study design
72 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 72 sample titles: 72 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

+ 32 more — browse all 72 samples with per-sample file links →

Datasets on a similar topic

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.