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Characterization of the cancer-associated field of injury in the nasal epithelium in never-smokers

GSE336399 Homo sapiens (human) RNA-seq (gene expression) 120 samples Submitted 2026/07/15 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 120 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)
Topic
Lung cancerThis series is part of our curated briefing on it.
Samples
120 samples
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
Characterization of the cancer-associated field of injury in the nasal epithelium in never-smokersLung cancer (Amsterdam, Netherlands) · 2026
Summary (written by the submitters)
Lung cancer in never-smokers represents a distinct and growing clinical challenge, yet early detection strategies largely target smokers. Here, we demonstrate that nasal epithelial gene expression alterations previously associated with lung cancer in current and former smokers are conserved in never-smokers. Notably, immune-related pathways, including interferon signaling, are similarly suppressed in malignant nodules in ever and never smokers.
Read the full summary ↓
These findings suggest shared molecular mechanisms of lung carcinogenesis in ever and never smokers and should motivate validation of airway-based biomarkers in never-smoker populations.
Published in
Characterization of the cancer-associated field of injury in the nasal epithelium in never-smokers
Lee M, Yokomizo M, Salehi-Rad R et al. · Lung cancer (Amsterdam, Netherlands) 2026 · PMID 42349086 · doi:10.1016/j.lungcan.2026.109506
This dataset
Open GSE336399 in finder → Find more human RNA-seq datasets → Lung cancer landscape →
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 GSE336399_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 PRJNA1481525. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42349086) ↗
Study design
A cohort of 120 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run UC-11 … UC-96 and carry no group information.

Samples in this study

+ 80 more — browse all 120 samples with per-sample file links →

More Lung cancer datasets

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.