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Activating NRF2^E79Q mutation alters the differentiation of human non-small cell lung cancer

GSE289043 Homo sapiens (human) RNA-seq (gene expression) 23 samples Submitted 2025/08/13 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 23 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
23 samples: 23 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.
Summary (written by the submitters)
The NRF2 signaling pathway promotes tumor initiation, progression and resistance to chemotherapy, radiation therapy and immune checkpoint inhibitors. The mechanisms underlying the biology of NRF2-active tumors are varied, and include altered cellular metabolism, a reductive shift in redox state, and immunosuppression. Here we determined the molecular and phenotypic impact of NRF2 activation on two human non-small cell lung cancer (NSCLC) cell models.
Read the full summary ↓
Inducible expression of NRF2^E79Q, a common activating NRF2 mutation, in H358 lung adenocarcinoma (LUAD) cells altered cellular morphology and increased xenograft tumor growth in mice but not in 2D cell culture. In contrast, NRF2^E79Q expression in H596 lung adeno-squamous cell carcinoma altered cellular morphology, increased neuroendocrine marker gene expression, but did not impact tumor growth in 2D or in xenografts. Gene expression profiling revealed shared and unique NRF2 transcriptional programs between these models, some of which were shared in primary lung tumors. Collectively, our findings reveal context-dependent effects of NRF2 activation on the growth and differentiation-state of two human NSCLC models, supporting a new differentiation-centric mechanism of NRF2-driven tumor progression and therapeutic resistance.
This dataset
Open GSE289043 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 GSE289043_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 PRJNA1220695 and SRA study SRP562247. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
23 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 23 sample titles: 23 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
More Lung cancer datasets

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.