GEO series

Transcriptomic Differences between Oral and Skin Keratinocytes

GSE270877 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2025/04/04 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 18 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: 18 samples: 6 conditions, mostly in triplicateGroups 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)

Compared to skin wounds, oral mucosal wounds heal more rapidly with less inflammation, faster re-epithelialization, and minimal scarring. One cell type that may be a differentiating factor is keratinocytes. Using immortalized skin and oral keratinocytes, HaCaT and TIGK, respectively, we found that oral keratinocytes have an enhanced migratory and proliferative capacity.

Read the full summary ↓

To examine the transcriptomic differences that underlie the improved healing abilities of oral keratinocytes, we generated an RNA-sequencing (RNA-seq) gene expression dataset utilizing HaCaT and TIGK. Differentially expressed genes (DEGs) were identified between HaCaT and TIGK at baseline and throughout the injury response. Gene Ontology (GO) or Reactome enrichment analysis was performed to understand the biological significance of DEGs. Processes related to inflammation and migration were uniquely enriched in TIGK. Additionally, TIGK retained its enhanced migratory capacity when grown on various basement membrane (BM) and extracellular matrix (ECM) substrates. This may be due to the differential expression of matrix metalloproteinases (MMPs) and integrins between HaCaT and TIGK. We also identified genes differentially over or under-expressed in HaCaT and TIGK following injury when compared to each respective cell type’s unwounded gene expression levels. Lastly, we showed that the post-injury secretome of TIGK promotes keratinocyte migration. Our comparative analyses suggest specific transcriptomic differences between oral and skin keratinocytes at unwounded baseline and in response to injury may underlie the distinct wound healing phenotypes seen in these two tissues. This work also provides a source of HaCaT and TIGK gene expression data over healing that can be used for future analyses.

This dataset

Open GSE270877 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 GSE270877_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 PRJNA1128674 and SRA study SRP516327. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

6 conditions, mostly in triplicate

HaCaT 0 h ×3 HaCaT 6h ×3 HaCaT 24h ×3 TIGK 0 h ×3 TIGK 6h ×3 TIGK 24h ×3

Supports a between-group comparison across 18 samples.

How we read this: 6 replicated groups read from 18 sample titles; they account for 18 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

GSM8354024 HaCaT 0 h-1 HaCaT 0 h
GSM8354025 HaCaT 0 h-2 HaCaT 0 h
GSM8354026 HaCaT 0 h-3 HaCaT 0 h
GSM8354027 HaCaT 6h-1 HaCaT 6h
GSM8354028 HaCaT 6h-2 HaCaT 6h
GSM8354029 HaCaT 6h-3 HaCaT 6h
GSM8354030 HaCaT 24h-1 HaCaT 24h
GSM8354031 HaCaT 24h-2 HaCaT 24h
GSM8354032 HaCaT 24h-3 HaCaT 24h
GSM8354033 TIGK 0 h-1 TIGK 0 h
GSM8354034 TIGK 0 h-2 TIGK 0 h
GSM8354035 TIGK 0 h-3 TIGK 0 h
GSM8354036 TIGK 6h-1 TIGK 6h
GSM8354037 TIGK 6h-2 TIGK 6h
GSM8354038 TIGK 6h-3 TIGK 6h
GSM8354039 TIGK 24h-1 TIGK 24h
GSM8354040 TIGK 24h-2 TIGK 24h
GSM8354041 TIGK 24h-3 TIGK 24h

Datasets on a similar topic

GSE308958 Comparative Analysis of Oral and Skin Keratinocytes Responses to Oxidative Stress 12 samples shares keratinocytes, oral, skin
GSE237520 Single-cell transcriptomic reconstruction of the human hair cycle: Capturing the temporal dynamics of skin tissue remodeling 22 samples shares oral, skin
GSE324461 Transcrptomic analysis of dysplastic oral keratinocytes infected with Fusobacterium polymorphum strains from patients with leukoplakia 19 samples shares keratinocytes, oral
GSE241132 Spatiotemporal Single-Cell Roadmap of Human Skin Wound Healing (Single-cell-Wound) 12 samples shares oral, skin
GSE255685 Age-related transcriptomic differences in peripheral blood of adolescents with major depressive disorder 279 samples shares differences
GSE268599 Astrocyte transcriptomic changes along the spatiotemporal progression of Alzheimer’s disease 146 samples shares oral
GSE311770 Minimally invasive 1 mm skin biopsies capture regional transcriptomic heterogeneity in vitiligo 105 samples shares skin
GSE154200 Skin transcriptome of non-communicable inflammatory skin diseases 376 samples shares skin

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.