GEO series

Validation of bronchial airway gene expression associated with bronchiectasis in nasal epithelium

GSE302095 Homo sapiens (human) RNA-seq (gene expression) 132 samples Submitted 2026/05/15 Platform GPL30173

At a glance

RNA-seq (gene expression) · human · 132 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: 132 samples: 132 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: Validation of bronchial airway gene expression associated with bronchiectasis in nasal epitheliumERJ open research · 2026

Summary (written by the submitters)

Objectives: Examination of bronchial epithelium-derived gene expression signature of bronchiectasis (BE) in nasal epithelium. Methods: We studied 220 participants from the Detection of Early Lung Cancer Among Military Personnel study with bulk RNA-seq of nasal epithelium brushings. Gene set enrichment analysis (GSEA) was used to examine whether genes previously identified as increased or decreased in the bronchial epithelium of individuals with radiologic BE are significantly enriched among the genes most significantly altered in the nasal epithelium.

Read the full summary ↓

GSEA and cell-type specific gene expression signatures were used to examine changes in the cellular composition of nasal epithelium associated with radiologic BE. Results: Genes previously found to have significantly altered expression in bronchial epithelium in radiologic BE are concordantly enriched among the genes most increased or decreased in nasal epithelium in radiologic BE in this validation set (p = 3x10-7, increased; p = 6x10-8, decreased). GSEA performed using cell-type signatures demonstrates that in nasal epithelium, BE is associated with increased multiciliated and deuterosomal cell-related gene expression and decreased expression of basal cell-related gene expression, consistent with our previous findings in bronchial epithelium. Conclusion: Our work validates our bronchial signature of BE in an independent dataset and demonstrates that similar gene expression changes are associated with radiologic BE in the bronchial and nasal epithelium. These findings support considering nasal epithelium brushing as a less invasive tool for screening and monitoring BE.

Published in

Validation of bronchial airway gene expression associated with bronchiectasis in nasal epithelium

Souery W, Wong J, Diaz AA et al. · ERJ open research 2026 · PMID 42094231 · doi:10.1183/23120541.00814-2025

This dataset

Open GSE302095 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 GSE302095_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 PRJNA1288513. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42094231) ↗

Study design

132 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 132 sample titles: 132 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

GSM9095940 4438-014-3-54
GSM9095941 4438-015-3-54
GSM9095942 4438-024-3-54
GSM9095943 4438-027-3-54
GSM9095944 4438-031-3-54
GSM9095945 4792-006-3-54
GSM9095946 4792-008-3-54
GSM9095947 4792-029-2-54
GSM9095948 4792-031-2-54
GSM9095949 4793-016-3-54
GSM9095950 4793-035-2-54
GSM9095951 4795-021-2-54
GSM9095952 4796-011-55
GSM9095953 4796-014-3-54
GSM9095954 4796-025-3-54
GSM9095955 4796-026-54
GSM9095956 4796-053-3-54
GSM9095957 4797-017-3-54
GSM9095958 4797-019-54
GSM9095959 4797-021-3-54
GSM9095960 4797-030-54
GSM9095961 4797-049-54
GSM9095962 4792-030-2-54
GSM9095963 4792-045-3-54
GSM9095964 4795-029-2-54
GSM9095965 4795-108-3-54
GSM9095966 4795-114-3-54
GSM9095967 4796-085-2-54
GSM9095968 4796-097-2-54
GSM9095969 4796-116-3-54
GSM9095970 4796-123-3-54
GSM9095971 4796-129-3-54
GSM9095972 4796-159-2-54
GSM9095973 4797-022-2-54
GSM9095974 4797-025-2-54
GSM9095975 4797-026-2-54
GSM9095976 4797-028-2-54
GSM9095977 4797-029-2-54
GSM9095978 4793-029-2-54
GSM9095979 4793-032-2-54

+ 92 more — browse all 132 samples with per-sample file links →

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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.