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Failed reprogramming of transformed cells due to induction of apoptosis and senescence impairs tumor progression in lung cancer

GSE245949 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2024/12/05 Platform GPL20301Platform GPL21103
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.
Paper
Inhibition of lung tumorigenesis by transient reprogramming in cancer cellsCell death & disease · 2024
Summary (written by the submitters)
Cell reprogramming to pluripotency applied to the study of cancer has identified transformation and pluripotency as two independent and incompatible cell fates. A detailed knowledge of the relationship between transformation and reprogramming could lead to the identification of new vulnerabilities and therapeutic targets in cancer. Here, we explore this interplay and find that OSKM expression limits tumor cell growth by inducing apoptosis and senescence.
Read the full summary ↓
We identify Oct4 and Klf4 as the main individual reprogramming factors responsible for this effect. Mechanistically, the induction of cell cycle inhibitor p21 downstream of the reprogramming factors acts as mediator of cell death and senescence. Using a variety of in vivo systems, including allografts, orthotopic transplantation and KRAS-driven lung cancer mouse models, we demonstrate that OSKM expression impairs tumor growth and reduces tumor burden
Published in
Inhibition of lung tumorigenesis by transient reprogramming in cancer cells
Pedrosa P, Zhang Z, Nuñez-Quintela V et al. · Cell death & disease 2024 · PMID 39587064 · doi:10.1038/s41419-024-07207-2
This dataset
Open GSE245949 in finder → Find more 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 GSE245949_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 PRJNA1030426 and SRA study SRP467617. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39587064) ↗
Study design
8 conditions, mostly in triplicate
4F_D1 ×3 4F_D3 ×3 TA_D1 ×3 TA_D3 ×3 day1_4F ×3 day1_TA ×3 day3_4F ×3 day3_TA ×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 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.