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C-ATF6 improves EGFR tyrosine kinase inhibitor sensitivity in non-small cell lung cancer via modulation of the calcium homeostasis network.

GSE285632 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2025/01/02 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 24 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
24 samples: 8 conditions, mostly in triplicateGroups 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)
Osimertinib (Osi) is an epidermal growth factor receptor tyrosine kinase inhibitor (EGFR-TKI), and its clinical efficacy is limited due to the emergence of resistance. We found that parental cells undergo a drug-tolerant persister (DTP) state before ultimately achieving acquired resistance. In this study, we restored the specifically dysregulated circular RNA (cATF6) in the DTP state and used it in combination with Osimertinib, finding that it could significantly synergistically target DTP tumor cells.
Read the full summary ↓
Mechanistically, it mainly disrupted the balance of Ca2+, ultimately leading to the death of DTP cells due to Ca2+ overload. This study reveals the impact of cATF6 on the dynamic balance of calcium ions, which may bring new strategies for epidermal growth factor receptor inhibitors (EGFR-TKIs) to eradicate DTP.
This dataset
Open GSE285632 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 GSE285632_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 PRJNA1204985 and SRA study SRP554721. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
8 conditions, mostly in triplicate
HCC827-DMSO ×3 HCC827-Osi ×3 HCC827-Parental-EV ×3 HCC827-Parental-OC ×3 HCC827-DTP-EV ×3 HCC827-DTP-OC ×3 HCC827-Resistant-EV ×3 HCC827-Resistant-OC ×3

Supports a between-group comparison across 24 samples.

How we read this: 8 replicated groups read from 24 sample titles; they account for 24 of them. 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.