GEO series

Development of a novel non-invasive biomarker panel for hepatic fibrosis in individuals with MASLD

GSE240729 Homo sapiens (human) RNA-seq (gene expression) 67 samples Submitted 2024/05/17 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 67 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: 67 samples: 67 × 7068-04-001Groups 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: Development of a novel non-invasive biomarker panel for hepatic fibrosis in MASLDNature communications · 2024

Summary (written by the submitters)

- Background & Aims: Considering the escalating prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) and MASLD-related fibrosis, accurate non-invasive biomarkers for diagnosis and staging of fibrosis are urgently needed. This study Aims to develop a blood-based biomarker panel for fibrosis detection in individuals with MASLD.

Read the full summary ↓

- Approach & Results: Using a translational diet-induced LDLr-/-.Leiden MASLD mouse model, candidate biomarkers were identified focused on the mechanism of collagen deposition, by integrating hepatic gene expression and new extracellular matrix deposition, as detected by dynamic D2O-labeling. To translate these findings to humans, gene expression profiles and biomarkers were analyzed in liver biopsies and serum samples from 67 individuals with histologically characterized MASLD and variable degrees of fibrosis. This led to the selection of three biomarkers for a blood-based fibrosis biomarker panel: IGFBP7, SSc5D and Sema4D. The accuracy of the biomarker panel was tested in a separate cohort of 128 individuals with histologically characterized MASLD across different stages of fibrosis. A Light Gradient Boosting Machine (LGBM) model was applied to predict fibrosis stage in MASLD (F0/F1: AUC = 0.90; F2: AUC = 0.91; F3/F4: AUC = 0.87). - Conclusion & Discussion: Using a translational Approach to identify collagen turnover related proteins indicative of fibrosis, we developed an accurate blood-based biomarker panel to detect and stage hepatic fibrosis in individuals with MASLD.

Published in

Development of a novel non-invasive biomarker panel for hepatic fibrosis in MASLD

Verschuren L, Mak AL, van Koppen A et al. · Nature communications 2024 · PMID 38811591 · doi:10.1038/s41467-024-48956-0

This dataset

Open GSE240729 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 GSE240729_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 PRJNA1005170 and SRA study SRP454922. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38811591) ↗

Study design

67 × 7068-04-001

Supports a between-group comparison across 67 samples.

How we read this: 1 replicated groups read from 67 sample titles; they account for 67 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

GSM7708388 7068-04-001-001 7068-04-001
GSM7708389 7068-04-001-002 7068-04-001
GSM7708390 7068-04-001-003 7068-04-001
GSM7708391 7068-04-001-004 7068-04-001
GSM7708392 7068-04-001-005 7068-04-001
GSM7708393 7068-04-001-006 7068-04-001
GSM7708394 7068-04-001-007 7068-04-001
GSM7708395 7068-04-001-008 7068-04-001
GSM7708396 7068-04-001-009 7068-04-001
GSM7708397 7068-04-001-010 7068-04-001
GSM7708398 7068-04-001-011 7068-04-001
GSM7708399 7068-04-001-012 7068-04-001
GSM7708400 7068-04-001-013 7068-04-001
GSM7708401 7068-04-001-014 7068-04-001
GSM7708402 7068-04-001-015 7068-04-001
GSM7708403 7068-04-001-017 7068-04-001
GSM7708404 7068-04-001-019 7068-04-001
GSM7708405 7068-04-001-020 7068-04-001
GSM7708406 7068-04-001-021 7068-04-001
GSM7708407 7068-04-001-022 7068-04-001
GSM7708408 7068-04-001-023 7068-04-001
GSM7708409 7068-04-001-024 7068-04-001
GSM7708410 7068-04-001-025 7068-04-001
GSM7708411 7068-04-001-026 7068-04-001
GSM7708412 7068-04-001-027 7068-04-001
GSM7708413 7068-04-001-028 7068-04-001
GSM7708414 7068-04-001-029 7068-04-001
GSM7708415 7068-04-001-030 7068-04-001
GSM7708416 7068-04-001-031 7068-04-001
GSM7708417 7068-04-001-032 7068-04-001
GSM7708418 7068-04-001-033 7068-04-001
GSM7708419 7068-04-001-035 7068-04-001
GSM7708420 7068-04-001-037 7068-04-001
GSM7708421 7068-04-001-038 7068-04-001
GSM7708422 7068-04-001-039 7068-04-001
GSM7708423 7068-04-001-040 7068-04-001
GSM7708424 7068-04-001-041 7068-04-001
GSM7708425 7068-04-001-042 7068-04-001
GSM7708426 7068-04-001-043 7068-04-001
GSM7708427 7068-04-001-044 7068-04-001

+ 27 more — browse all 67 samples with per-sample file links →

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Search all human 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.