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Metabolic dysfunction-associated steatohepatitis reduces interferon and macrophage liver gene signatures in chronic HBV patients

GSE274114 Homo sapiens (human) RNA-seq (gene expression) 39 samples Submitted 2024/10/31 Platform GPL20301Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 39 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
39 samples: 11 × ENEG vs 10 × NASH vs 9 × ENEG_NASH vs 9 × CTRLGroups 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)
Background & Aims: Chronic HBV patients with concomitant metabolic dysfunction-associated steatohepatitis (MASH) have been shown to develop more advanced fibrosis faster with more severe liver disease as compared to patients with chronic HBV alone. However, our understanding of the underlying mechanisms is limited. Here we study how MASH co-morbidity impact immune activity in the liver of patients with chronic HBV infection.
Read the full summary ↓
Methods: Bulk RNA sequencing was performed on liver biopsies from patients with only MASH (n=10), only HBeAg-negative chronic HBV (ENEG; n=11), combined MASH/ENEG (n=9) and healthy controls (n=9). Biopsies with no or minimal fibrosis (≤F2) were selected to avoid confounding effects of fibrosis. We compared whole transcriptome data from patients with MASH/ENEG to those with ENEG alone to determine the impact of MASH co-morbidity on chronic hepatitis B. Results: There is a high degree of overlap of liver gene expression profiles in patients with only ENEG versus those with only MASH compared to healthy controls, suggesting a largely shared mechanism of liver dysfunction and immune activity for these distinct conditions. In patients with ENEG, MASH co-morbidity significantly reduced interferon pathway activity (NES=2.03, p.adj=0.0251), the expression of ISGs (e.g., IFIT2, IFI27, IFITM1, IFI6), and macrophage gene signatures (e.g., MARCO, CD163, CD5L, CD63), when compared to patients with ENEG alone. Conclusions: Transcriptomic profiling of the liver suggests that MASH negatively impacts ISGs expression in the liver of patients with ENEG, which may affect antiviral immune pathways, viral replication and inflammatory responses resulting in an increased risk of advanced fibrosis in patients with chronic hepatitis B. Our study provides valuable insights for guiding future research aimed at developing effective, tailored strategies for managing patients with both conditions.
This dataset
Open GSE274114 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 GSE274114_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 PRJNA1144975 and SRA study SRP524555. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
11 × ENEG vs 10 × NASH vs 9 × ENEG_NASH vs 9 × CTRL

Supports a between-group comparison across 39 samples.

How we read this: 4 replicated groups read from 39 sample titles; they account for 39 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
Datasets on a similar topic

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