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mRNA expression profiling of A549 cells treated with TSA at different time points

GSE244427 Homo sapiens (human) RNA-seq (gene expression) 10 samples Submitted 2026/04/26 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 10 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
10 samples: 5 conditions, mostly in duplicateGroups 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)
Cellular senescence (CS) refers to a stable arrest of the cell cycle characterized by an altered profiles of gene expression networks. However, a reliable strategy to quantify senescent levels is lacking. Here we developed a machine learning-based tool (Predictive Cellular Senescence Model, PreCSenM) that addresses this issue by defining senescent levels as CS score through incorporating senescent features from multiplatform data.
Read the full summary ↓
While applying this model to predict clinical outcomes in patients with lung adenocarcinoma (LUAD), PreCSenMo identified that HDAC inhibitors (HDACi) can trigger LUAD cellular senescence. To reveal the transcriptional profiling dynamics of HDACi-induced senescence in lung cancer cells, we performed RNA-seq of A549 treated with TSA at different time points.
This dataset
Open GSE244427 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 GSE244427_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 PRJNA1023030 and SRA study SRP464097. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
5 conditions, mostly in duplicate
A549_TSA_Control ×2 A549_TSA_6h ×2 A549_TSA_12h ×2 A549_TSA_24h ×2 A549_TSA_48h ×2

Supports a between-group comparison across 10 samples.

How we read this: 5 replicated groups read from 10 sample titles; they account for 10 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.