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Effect of glucose deprivation and silencing of transcription factors ATF4 and ATF6 on gene expression in human lung adenocarcinoma cell line A549

GSE317139 Homo sapiens (human) RNA-seq (gene expression) 36 samples Submitted 2026/06/30 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 36 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
36 samples: 36 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)
Tumours grow faster than the local vasculature, resulting in a shortage of oxygen and nutrients. To explore how lung cancer cells cope with a low-glucose environment, we analysed transcriptional changes in glucose-starved A549 cells using RNA-sequencing. This showed downregulation of multiple pathways related to DNA replication and cell cycle, and regulation of metabolic gene sets.
Read the full summary ↓
The most upregulated transcriptional response to glucose deprivation was the Unfolded Protein Response (UPR), which has been shown to promote adaptation to proteotoxic and endoplasmic reticulum stress. Glucose shortage robustly induced the three branches of the UPR in several non-small cell lung carcinoma cell lines, as indicated by ATF4 accumulation, XBP1 mRNA splicing and ATF6 cleavage. ATF6 and ATF4 silencing revealed that these factors cooperate to regulate multiple metabolic genes and pathways related to lipid synthesis and to amino acid synthesis and transport. ATF4 additionally regulated the transcriptional induction of mitochondrial OXPHOS-associated pathways.
This dataset
Open GSE317139 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 GSE317139_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 PRJNA1406152 and SRA study SRP665183. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
36 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 36 sample titles: 36 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.