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Transcriptomic and Epigenomic Signatures Distinguish High- and Low-Risk Endotypes for Liver Tumor Development [RNA-Seq liver]

GSE272929 Mus musculus (mouse) RNA-seq (gene expression) 75 samples Submitted 2024/12/04 Platform GPL21493Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 75 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
Mus musculus (mouse)
Samples
75 samples: 74 distinct titles, 1 of them repeatedGroups 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
Transcriptomic and epigenomic signatures distinguish high- and low-risk endotypes for liver tumor developmentEnvironmental and molecular mutagenesis · 2024
Summary (written by the submitters)
The epigenome is a target for environmental exposures, and a potential determinant of inter-individual differences in response. In genetically identical C57Bl/6 mice exposed from gestation to weaning to the endocrine disrupting chemical (EDC) tributyltin (TBT), tumor development later in life varied across multiple cohorts over time and depending on sex and diet.
Read the full summary ↓
In one cohort where approximately half of TBT-exposed male mice developed tumors at 10 months (Katz, 2020), transcriptomic (RNA-seq) and epigenomic (ChIP-seq) profiling was performed on blood and liver tissue from mice that developed tumors (i.e. “high-risk”) and equivalently exposed mice did not (i.e “low-risk”). Blood transcriptomic signatures separated TBT-exposed from vehicle controls but did not discriminate between animals that developed tumors vs those that did not. However, uninvolved liver tissue of mice with tumors exhibited transcriptomic and epigenomic signatures distinct from liver tissue of mice without tumors and had many features in common with tumors. These high-risk transcriptomic and epigenomic features were also found in 10/26 TBT-exposed mice at 5 months, indicating that this risk signature preceded tumor development. Thus, while early life exposure to TBT exhibits variable penetrance for tumor development, indicating TBT-exposure is not sufficient for tumorigenesis, increased risk for tumor development is linked to epigenomic and transcriptomic reprogramming of the liver induced by this EDC.
Published in
Transcriptomic and epigenomic signatures distinguish high- and low-risk endotypes for liver tumor development
Grimm SL, Talley T, Jangid RK et al. · Environmental and molecular mutagenesis 2024 · PMID 39588573 · doi:10.1002/em.22639
This dataset
Open GSE272929 in finder → Find more mouse RNA-seq datasets →
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 GSE272929_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 PRJNA1139320 and SRA study SRP521812. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39588573) ↗
Study design
74 distinct titles, 1 of them repeated
Liver_3M3V_S ×2

How we read this: Read from 75 sample titles: 74 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

+ 35 more — browse all 75 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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