GEO series

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

GSE272930 Mus musculus (mouse) RNA-seq (gene expression) 15 samples Submitted 2024/12/04 Platform GPL21493

At a glance

RNA-seq (gene expression) · mouse · 15 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: 15 samples: 15 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.
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 GSE272930 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 GSE272930_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 PRJNA1139319 and SRA study SRP521796. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39588573) ↗

Study design

15 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 15 sample titles: 15 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

GSM8415289 1M10T-BLOOD
GSM8415290 1M10V-BLOOD
GSM8415291 2M10T-BLOOD
GSM8415292 2M10V-BLOOD
GSM8415293 3M10T-BLOOD
GSM8415294 3M10V-BLOOD
GSM8415295 4M10T-BLOOD
GSM8415296 4M10V-BLOOD
GSM8415297 5M10T-BLOOD
GSM8415298 5M10V-BLOOD
GSM8415299 6M10T-BLOOD
GSM8415300 6M10V-BLOOD
GSM8415301 7M10T-BLOOD
GSM8415302 7M10V-BLOOD
GSM8415303 8M10T-BLOOD

Datasets on a similar topic

GSE272929 Transcriptomic and Epigenomic Signatures Distinguish High- and Low-Risk Endotypes for Liver Tumor Development [RNA-Seq liver] 75 samples shares distinguish, epigenomic, signatures, endotypes, low-risk, rna-seq, liver
GSE272931 Transcriptomic and Epigenomic Signatures Distinguish High- and Low-Risk Endotypes for Liver Tumor Development 307 samples shares distinguish, epigenomic, signatures, endotypes, low-risk, liver
GSE272927 Transcriptomic and Epigenomic Signatures Distinguish High- and Low-Risk Endotypes for Liver Tumor Development [ChIP-Seq] 217 samples shares distinguish, epigenomic, signatures, endotypes, low-risk, liver
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GSE279536 Hypoxic stress dysregulates functions of glioma-associated myeloid cells through epigenomic and transcriptional programs [RNA-seq] 38 samples shares epigenomic, rna-seq

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