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Multidimensional Exposure Architecture Shapes Vaping-Associated Transcriptomic Dysregulation in Oral Epithelium

GSE330022 Homo sapiens (human) RNA-seq (gene expression) 83 samples Submitted 2026/06/24 Platform GPL18573
At a glance

RNA-seq (gene expression) · human · 83 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
83 samples: 83 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
Multidimensional exposure architecture shapes vaping-associated transcriptomic dysregulation in oral epitheliumFrontiers in oncology · 2026
Summary (written by the submitters)
Electronic cigarette (e-cig) use (vaping) has been associated with dysregulation of genes and molecular pathways in epithelial tissues. However, the relative contributions of dose and product characteristics to vaping-associated transcriptomic alterations have not been systematically evaluated. We performed RNA-sequencing of oral epithelial cells from e-cig users (vapers), cigarette smokers, and non-users.
Read the full summary ↓
Differential gene expression was assessed using covariate-adjusted limma-voom modeling with false discovery rate control. We evaluated the extent to which exposure-specific dose metrics (including cumulative e-liquid, cumulative e-nicotine, years vaped, and plasma cotinine for vaping, and pack-years and plasma cotinine for smoking) explained transcriptional changes. Among vapers, we additionally examined whether device generation and flavor type contributed to variation in gene expression. Both vaping and smoking were associated with transcriptomic dysregulation relative to non-users, with partial overlap in differentially expressed genes (DEGs). Functional enrichment analyses revealed disruption of shared cancer- and signaling pathways, including RHO GTPase Cycle, as well as perturbation of pathways specific to vapers or smokers. Among vapers, 27.6% of DEGs showed concordant behavior across all dose metrics, indicating heterogeneous dose-response patterns for the remaining DEGs. Device generation and flavor type explained additional, largely non-overlapping components of gene expression variability. A much higher proportion of smoking-associated DEGs (54.1%) was consistently affected across dose metrics, reflecting more unified dose-dependent responses. These findings suggest that vaping-associated transcriptional dysregulation reflects combined influences of dose and product characteristics, highlighting structural differences in molecular perturbations between vaping and smoking. Incorporating multidimensional exposure metrics and product features into regulatory evaluation may better capture the biological complexity of e-cig exposure, thus informing clinical, public health practice, and regulatory decisions.
Published in
Multidimensional exposure architecture shapes vaping-associated transcriptomic dysregulation in oral epithelium
George J, Tommasi S, Pabustan N et al. · Frontiers in oncology 2026 · PMID 42306798 · doi:10.3389/fonc.2026.1838256
This dataset
Open GSE330022 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 GSE330022_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 PRJNA1462574 and SRA study SRP697856. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42306798) ↗
Study design
83 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 83 sample titles: 83 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

+ 43 more — browse all 83 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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