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Transcriptomic analysis reveals the protective biomarkers of Pien Tze Huang in liver cirrhosis mice model [circRNA]

GSE269012 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2024/06/10 Platform GPL21103
At a glance

RNA-seq (gene expression) · mouse · 18 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
18 samples: 18 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.
Summary (written by the submitters)
Cirrhosis has been one of the major causes of morbidity and mortality worldwide, but safe and effective therapy remains limited. A traditional Chinese medicine, Pien Tze Huang (PTH), has been reported to be effective in protecting the liver. Thus, in this study, we established a mouse cirrhosis model to investigate the effect of PTH on cirrhosis and the potential protective biomarkers using transcriptomic analysis.
Read the full summary ↓
We identified 1689, 218, 61, and 145 differentially expressed mRNAs, lncRNAs, miRNAs, and circRNAs between the PTH treatment group and the cirrhosis group. Functional enrichment analysis demonstrated that biologically sensible GO terms, such as positive regulation of biological process and response to stimulus, and KEGG pathways, such as IL-17 signaling pathway and Steroid hormone biosynthesis, might be associated with PTH treatment. Similarity analysis based on the drug-induced gene expression profiles of Connectivity Map (version 2) suggested that PTH might have a similar effect to several drugs in clinical trials for cirrhosis. Finally, integration analysis of the competitive endogenous RNA network revealed that eight genes, i.e., P4ha1, Runx1, Inpp5d, Trp53inp1, Rrad, Pkm, Eva1a, and Jun, might be the potential protective biomarkers of PTH against cirrhosis.
This dataset
Open GSE269012 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 GSE269012_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 PRJNA1119957 and SRA study SRP511607. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
18 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 18 sample titles: 18 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
Datasets on a similar topic

Search all mouse 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.