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Blood Gene Expression Profiling in Patients with Idiosyncratic Drug-Induced Liver Injury

GSE275008 Homo sapiens (human) RNA-seq (gene expression) 78 samples Submitted 2025/04/16 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 78 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)
Samples
78 samples: 77 × GTGroups 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
Blood toxicogenomics reveals potential biomarkers for management of idiosyncratic drug-induced liver injuryFrontiers in genetics · 2025
Summary (written by the submitters)
Accurate diagnosis, assessment, and prognosis of idiosyncratic drug-induced liver injury (IDILI) is problematic, in part due to the shortcomings of traditional biomarkers. Studies in rodents and healthy volunteers have supported that RNA transcript changes in whole blood may address some of these shortcomings. In this study, we conducted RNA-Seq analysis on peripheral blood samples collected from 55 patients with acute IDILI and 17 patients with liver injuries not attributed to IDILI.
Read the full summary ↓
Three differentially expressed genes (DEGs; CFD, SQLE, and INKA1) were identified as significantly associated with IDILI vs other liver injuries. No DEGs were identified comparing IDILI patients to the 5 patients with autoimmune hepatitis, supporting common underlying mechanisms. Two genes (VMO1 and EFNA1) were significantly associated with hepatocellular injury compared to mixed/cholestatic injury. When patients with severe vs milder IDILI were compared, we identified over 500 DEGs. The top pathways identified from these DEGs had in common down regulation of multiple T-cell specific genes. Further analyses confirmed that these changes could largely be accounted for by a fall in the concentration of circulating T-cells during severe DILI, perhaps due to exhaustion or sequestration of these cells in the liver. Exploration of DEGs specific for the individual causal agents was largely unsuccessful, but isoniazid-induced IDILI demonstrated 25 DEGs compared to other non-isoniazid IDILI cases. Finally, among the 14 IDILI patients that had hepatocellular jaundice (i.e. Hy’s Law ases), we identified 39 DEGs between the 4 patients with a fatal or liver transplantation outcomes compared to those that recovered. These findings suggest the potential for blood-based transcriptomic biomarkers to aid in the diagnosis and prognostic stratification of IDILI.
Published in
Blood toxicogenomics reveals potential biomarkers for management of idiosyncratic drug-induced liver injury
Church RJ, Anchang B, Bennett BD et al. · Frontiers in genetics 2025 · PMID 40201567 · doi:10.3389/fgene.2025.1524433
This dataset
Open GSE275008 in finder → Find more human 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 GSE275008_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 PRJNA1148952 and SRA study SRP526845. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40201567) ↗
Study design
77 × GT

Supports a between-group comparison across 77 samples.

How we read this: 1 replicated groups read from 78 sample titles; they account for 77 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

+ 38 more — browse all 78 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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