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Transcriptomic Profiling of Psoriatic Lesions by Tape-Stripping Reveals Site-Specific Differences

GSE312703 Homo sapiens (human) RNA-seq (gene expression) 44 samples Submitted 2026/05/22 Platform GPL28038
At a glance

RNA-seq (gene expression) · human · 44 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
44 samples: 44 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)
Psoriasis is a chronic immune-mediated skin disease, with plaque psoriasis being its most prevalent form. Although biologic therapies have significantly improved outcomes for patients with moderate to severe disease, certain anatomical regions often remain resistant to treatment. Given the observed variability in treatment response depending on lesion location, we aimed to explore anatomical site-specific differences in the skin transcriptome.
Read the full summary ↓
Using a non-invasive tape-stripping technique, we collected and analysed 44 psoriatic plaque samples from the scalp, trunk, upper extremities, and lower extremities, followed by differential gene expression and gene ontology analysis. Moreover, we included 80 samples obtained from healthy skin biopsies (GSE54456) to conduct an inflammation-controlled approach. We used two different approaches, an intra-disease approach, in which anatomically different sites were compared, and an inflammation-controlled approach, in which the inflammation bias was minimized. Our findings revealed distinct molecular signatures and biological pathways across different anatomical sites, including differential expression of SERPINB7 and miRNAs such as miR-205 and miR-203a, along with different pathways. Furthermore, our results emphasized the heterogeneity of psoriasis and suggested that site-specific molecular mechanisms may contribute to variations in disease manifestation and treatment response. This study underscores the importance of developing more personalized, site-specific therapeutic strategies.
This dataset
Open GSE312703 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 GSE312703_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 PRJNA1375753 and SRA study SRP651792. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
44 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 44 sample titles: 44 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

+ 4 more — browse all 44 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.