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Identification of Pre-Disease State in NC/Nga Mice with Atopic dermatitis-like Symptoms using Dynamical Network Biomarkers

GSE295168 Mus musculus (mouse) RNA-seq (gene expression) 190 samples Submitted 2025/09/09 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 190 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
Mus musculus (mouse)
Samples
190 samples: 20 conditions, mostly with about 10 replicates eachGroups 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
Identification of Pre-Disease State in NC/Nga Mice with Atopic Dermatitis-Like Symptoms Using Dynamical Network BiomarkersThe Journal of investigative dermatology · 2026
Summary (written by the submitters)
Atopic dermatitis (AD) is a common skin disease worldwide that is characterized by itchy eczema that undergoes cycles of exacerbation and remission. Although several biomarkers reflect AD severity, reliable markers for predicting disease onset and recurrence remain unclear. The dynamical network biomarker theory detects pre-disease states or pre-symptomatic states by analyzing characteristic network fluctuations just before phase transitions from healthy states to disease states.
Read the full summary ↓
To investigate the applicability of dynamical network biomarker theory to AD, we conducted blood transcriptome analysis in NC/Nga mice before and after the onset of AD-like dermatitis. We identified 356 dynamical network biomarker genes whose temporal fluctuations enabled the detection of the pre-disease state preceding AD-like dermatitis onset. These genes were significantly enriched in pathways related to endoplasmic reticulum stress and apoptosis; fluctuations in these pathways may be associated with the onset of AD. Our study indicates that the dynamical network biomarker theory may be applicable to AD and that identifying a pre-disease state may enable early intervention and disease prevention. Collectively, this approach could provide valuable insights into maintaining long-term remission and improving AD management strategies.
Published in
Identification of Pre-Disease State in NC/Nga Mice with Atopic Dermatitis-Like Symptoms Using Dynamical Network Biomarkers
Sensui T, Iwasaki K, Aihara K · The Journal of investigative dermatology 2026 · PMID 40992684 · doi:10.1016/j.jid.2025.09.009
This dataset
Open GSE295168 in finder → Find more mouse 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 GSE295168_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 PRJNA1253655 and SRA study SRP580316. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40992684) ↗
Study design
20 conditions, mostly with about 10 replicates each
blood, day 2, group 1 # ×10 blood, day 44, group 1 # ×10 blood, day 5, group 2 # ×10 blood, day 40, group 2 # ×10 blood, day 9, group 3 # ×10 blood, day 44, group 3 # ×10 blood, day 12, group 4 # ×10 blood, day 40, group 4 # ×10 +12 more

Supports a between-group comparison across 190 samples.

How we read this: 20 replicated groups read from 190 sample titles; they account for 190 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

+ 150 more — browse all 190 samples with per-sample file links →

Datasets on a similar topic

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