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Integrative transcriptomic, network, and genomic analysis of peripheral blood mononuclear cells identifies candidate gene drivers of dupilumab clinical response in atopic dermatitis patients

GSE326320 Homo sapiens (human) RNA-seq (gene expression) 36 samples Submitted 2026/06/05 Platform GPL28038
At a glance

RNA-seq (gene expression) · human · 36 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
36 samples: 36 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.
Summary (written by the submitters)
Atopic dermatitis (AD) is among the most common chronic inflammatory diseases. Due to the heterogeneous presentation of AD, patient response to treatment may differ considerably. Therefore, there is a pressing need for biomarkers that could predict response to biological therapies. In line with this, we aimed to identify blood biomarkers that could predict response in patients treated with dupilumab.
Read the full summary ↓
The present study applied a multi-stage integrative analytical framework combining transcriptomic profiling, functional enrichment, co-expression network analysis, and genomic variant analysis to identify potential biomarkers. Eighteen dupilumab-naïve patients were enrolled in the transcriptomic analysis, with blood samples collected at baseline and after 16–18 weeks of therapy; five patients were identified as non-responders. Additionally, genotyping was performed in 34 patients. We identified a set of candidate genes (RPL18A, RPS28, FAU, MASTL, AURKA, TAF2, BUB1B, and RNF135) and genomic variants that may reflect underlying biological mechanisms influencing therapeutic response. Finally, our study highlighted potential genes that could predict outcome in patients with AD who are treated with dupilumab. Moreover, our study represents an incremental contribution to existing knowledge and opens new avenues for research that may ultimately lead to personalized medicine.
This dataset
Open GSE326320 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 GSE326320_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 PRJNA1445105 and SRA study SRP687704. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
36 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 36 sample titles: 36 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
Datasets on a similar topic

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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.