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Identifying Potential Biomarkers Associated with Sebum Production, Pore Enlargement, and Comedones Using Transcriptomic Approaches

GSE308390 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2025/09/18 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 24 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
24 samples: 24 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)
The molecular mechanisms underlying skin pore issues—excessive sebum secretion, pore enlargement, and comedone formation—remain poorly defined. We collected skin samples from four groups—dry skin without enlarged pores (DNEP), oily skin without pores (ONEP), oily skin with enlarged pores (OEP), oily skin with pores and comedones (OEPC)—measured physiological parameters, identified differentially expressed genes (DEGs) via RNA sequencing, conducted KEGG/GO enrichment analysis for DEGs, and used canonical correlation analysis to explore gene expression-physiological parameter relationships.
Read the full summary ↓
Group comparisons revealed key genes and pathways: sebum production (DNEP vs. ONEP) involved lipid synthesis and inflammation, with ADM and PLAUR correlating with sebum content. Pore enlargement (ONEP vs. OEP) implicated ECM remodeling and immune processes, with IL27RA, MTOR, and PTAFR linked to skin roughness and elasticity. Comedogenesis (OEP vs. OEPC) involved lipid metabolism and differentiation, with ACADVL and CHST11 correlating with sebum and roughness. This study highlights seven core genes (ADM, PLAUR, IL27RA, MTOR, PTAFR, ACADVL, CHST11) and immune-inflammatory/ECM-receptor pathways as critical drivers of pore phenotypes, suggesting promising therapeutic targets.
This dataset
Open GSE308390 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 GSE308390_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 PRJNA1330326 and SRA study SRP621350. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
24 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 24 sample titles: 24 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

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.