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Liver sinusoidal scores: a new tool for the diagnosis and prognosis of chronic liver disease

GSE280687 Homo sapiens (human) Single-cell RNA-seq 23 samples Submitted 2026/04/15 Platform GPL16791
At a glance

Single-cell RNA-seq · human · 23 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
23 samples: 12 × CH vs 11 × CTGroups 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
Sinusoidal cell-derived biomarker scores predict diagnosis and prognosis in chronic liver diseaseBMC medicine · 2026
Summary (written by the submitters)
Background and Aims: Sinusoidal cells drive the progression of chronic liver disease (CLD). Therefore, novel biomarkers for CLD should reflect the state of sinusoidal cells. However, the liver is mainly constituted from hepatocytes (80-90% of cells), while sinusoidal cells only represent the 10% of the cellular populations. Therefore, determination of biomarkers from plasma or bulk transcriptomics underrepresents smaller sinusoidal cell subpopulations, such as liver sinusoidal endothelial cells (LSECs), hepatic stellate cells (HSCs), and macrophages (MPs).
Read the full summary ↓
On the other hand, recent studies performing single-cell sequencing allow for finer phenotype determination, but these analyses are too expensive for routine implementation in patient care. The aim of this study is to propose biologically-relevant markers of liver sinusoidal phenotype for the diagnosis and prognosis of CLDs. Methods: We estimated the relevant sinusoidal cell subpopulations in an internal cohort of 11 controls and 12 cirrhotic livers by gene deconvolution. Then, we reanalyzed published single-cell sequencing data in order to find a gene signature specific to these subpopulations. We combined the expression of these sinusoidal genes into 3 sinusoidal scores (endothelial, mesenchymal and macrophage) correlating with patient phenotypes. The ability of these scores to classify patients and predict fibrosis progression, regression and clinical decompensation was assessed in an internal cohort of 113 patients and further validated in 3 external cohorts for a total of 1008 patients with F3-F4 CLD (Simtuzumab, ATLAS & STELLAR trials). Results: We obtained a sinusoidal gene signature which was more specific for than traditional markers of LSEC capillarization, HSC activation and macrophage polarization. The three sinusoidal scores generated with these gene signatures were significantly higher in those patients with worse clinical parameters in our internal cohort and in the external validation cohort. The sinusoidal scores at baseline were significantly higher in patients that progressed from F3 to F4 fibrosis stage after 96 weeks of follow-up compared to those that stayed at F3. Similarly, the baseline sinusoidal scores were significantly lower in those patients that regressed from F4 to F3 compared to those that stayed at F4. Finally, the scores were able to predict decompensation in the following 96 weeks of follow-up (AUROC = 0.98) in the validation cohort and correlated with conventional parameters for evaluation of clinical decompensation (HVPG, Child-Pugh and MELD). Conclusions: This sinusoidal gene panel could aid in the diagnosis and prognosis of patients with CLD and predict future complications of cirrhosis. This technique can be easily assessed by RT-PCR and could be a useful tool for clinical decision making in the future, possibly aiding in assessment of drug response or in choosing the most relevant cell target for therapy.
Published in
Sinusoidal cell-derived biomarker scores predict diagnosis and prognosis in chronic liver disease
Guixé-Muntet S, Fernández-Iglesias A, Lopez D et al. · BMC medicine 2026 · PMID 41764477 · doi:10.1186/s12916-026-04733-y
This dataset
Open GSE280687 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 GSE280687_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1179965 and SRA study SRP542172. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41764477) ↗
Study design
12 × CH vs 11 × CT

Supports a between-group comparison across 23 samples.

How we read this: 2 replicated groups read from 23 sample titles; they account for 23 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
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