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Whole Transcriptome Profiling of Lesional Psoriatic and Adjacent Normal Skin Tissues

GSE244679 Homo sapiens (human) RNA-seq (gene expression) 48 samples Submitted 2026/03/12 Platform GPL20301
At a glance

RNA-seq (gene expression) · human · 48 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
48 samples: 48 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
Genome-wide RNA-seq, DNA methylation and small RNA-seq analysis unraveled complex gene regulatory networks in psoriasis pathogenesisGene · 2025
Summary (written by the submitters)
Purpose: To identify the differentially expressed genes in lesional skin of psoriasis patients compared to adjacent normal skin tissue by Next Generation Sequencing. Methods: Total RNA was isolated from 24 paired lesional and adjacent normal skin tissue samples and Next Generation Sequencing was performed. The sequences reads were trimmed of adapter sequences and quality trimmed using CutAdapt, followed by alignment to the human genome (hg19/GRCh37) by 'STAR (v2.7.10a)'.
Read the full summary ↓
The aligned files were sorted based on chromosomal location. Trancript assembly was performed using StringTie (v1.3.3b) and python script prepDE.py was used to obtain the raw read counts for all annotated genes. Differnetial expression analysis was performed with the raw reads using R-package 'edgeR'. Gene ontology with the deregulated genes was performed uign DAVID GO. The differentially expressed genes were integrated with differentially expressed miRNAs (GSE183547) and differentially methylated CpGs (GSE115797) to identify the genes regulated by miRNA and DNA metylation respectively. Results: We identified 1,541 differentially expressed protein-coding genes with at least 2-fold deregulation that were statistically significant (FDR ≤ 0.05). The deregulated genes were found to be associated with biological processes including inflammatory response, cytokine mediated signaling, keratinization, neutrophil chemotaxis and defense response to virus among the top enriched processes. Inetgrative analysis with small RNA-seq and DNA methylation profiling using Illumina Infinum 450K BeadArray identified 487 and 321 genes to be regulated by miRNA and DNA methylation respectively. Conclusions: Our multi-omics integrative analysis identfied the biological processes regulated exclusively as well as combinatorially by miRNAs and DNA methylation in the disease pathogenesis of psoriasis.
Published in
Genome-wide RNA-seq, DNA methylation and small RNA-seq analysis unraveled complex gene regulatory networks in psoriasis pathogenesis
Laha S, Das S, Banerjee U et al. · Gene 2025 · PMID 39233195 · doi:10.1016/j.gene.2024.148903
This dataset
Open GSE244679 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 GSE244679_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 PRJNA1024385 and SRA study SRP464649. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39233195) ↗
Study design
48 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 48 sample titles: 48 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

+ 8 more — browse all 48 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.