GEO series

Obesity Promotes Lung Carcinogenesis Through Airway Immune Dysfunction

GSE336982 Homo sapiens (human) RNA-seq (gene expression) 183 samples Submitted 2026/08/05 Platform GPL18573

At a glance

RNA-seq (gene expression) · human · 183 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: 183 samples: 183 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: Obesity Promotes Lung Carcinogenesis Through Airway Immune DysfunctionJournal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer · 2026

Summary (written by the submitters)

Rationale: Despite clear links between obesity and dysfunctional anti-tumor immunity, a high Body Mass Index has been associated with decreased lung cancer risk, leading to an “Obesity Paradox”. Objective: We sought to clarify this relationship and gain mechanistic insights using both clinical and preclinical modeling. Methods & Measurements: A case control study of early-stage lung cancer risk was conducted in 1170 high-risk individuals (594 with cancer) stratified by total abdominal fat area (TFA) determined using CT scans.

Read the full summary ↓

Airway gene expression data from 278 additional patients was analyzed as a function of TFA, and obesity’s impact on chemical- and mutation-driven lung carcinogenesis models was examined. Effects on the immune contexture of murine lungs and patient broncho-alveolar lavage (n=109) were profiled using flow cytometry. Results: Lung cancer was linked to higher TFA in univariate and multivariable models. Gene expression in high-TFA airways suggested suppressed immune activity. Compared to lean controls, the lungs of obese mice displayed abundant potentially suppressive myeloid cells, activated Regulatory T cells, and effector T cell deficits at baseline and during carcinogenesis, which was significantly exacerbated by obesity. Functional assays and depletion studies suggested obesity-enhanced Tregs can potentiate rapid tumor growth, and flow analysis of patients airway leukocytes further suggest compromised lung immune surveillance in patients of high TFA. Conclusions: Obesity is associated with enhanced lung carcinogenesis. Mechanisms of immune suppression upregulated with excess adiposity, including activated, functional Tregs, are evident in clinical and preclinical systems. These results refute the concept of an obesity paradox for lung cancer risk.

Published in

Obesity Promotes Lung Carcinogenesis Through Airway Immune Dysfunction

Barbi J, Smith RJ Jr, Vedire YR et al. · Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer 2026 · PMID 42379297 · doi:10.1016/j.jtho.2026.104066

This dataset

Open GSE336982 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 GSE336982_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 PRJNA1483656. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42379297) ↗

Study design

183 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 183 sample titles: 183 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

GSM9845655 4790-027-60
GSM9845656 4795-107-60
GSM9845657 4795-011-60
GSM9845658 4238-017-60
GSM9845659 4714-005-60
GSM9845660 4795-126-60
GSM9845661 4438-111-60
GSM9845662 4790-005-60
GSM9845663 4714-021-60
GSM9845664 4238-014-60
GSM9845665 4714-023-60
GSM9845666 4798-007-60
GSM9845667 4798-015-60
GSM9845668 4794-012-60
GSM9845669 4238-004-60
GSM9845670 4202-008-60
GSM9845671 4795-026-60
GSM9845672 4714-008-60
GSM9845673 4795-104-60
GSM9845674 4202-024-60
GSM9845675 4238-042-60
GSM9845676 4714-011-60
GSM9845677 4798-016-60
GSM9845678 4790-007-60
GSM9845679 4795-059-60
GSM9845680 4794-003-60
GSM9845681 4202-020-60
GSM9845682 4795-073-60
GSM9845683 4793-001-60
GSM9845684 4798-010-60
GSM9845685 4790-074-60
GSM9845686 4796-041-60
GSM9845687 4795-070-60
GSM9845688 4791-007-60
GSM9845689 4202-005-60
GSM9845690 4795-008-60
GSM9845691 4796-017-63
GSM9845692 4790-003-60
GSM9845693 4202-003-60
GSM9845694 4202-022-60

+ 143 more — browse all 183 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.