← BioTransfer GEO Dataset Finder
GEO series

Multiomic analysis uncovers a continuous spectrum of differentiation and Wnt-MDK-driven immune evasion in hepatoblastoma [RNA-seq]

GSE261956 Homo sapiens (human) Single-cell RNA-seq 52 samples Submitted 2025/02/07 Platform GPL30173
At a glance

Single-cell RNA-seq · human · 52 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
52 samples: 17 conditions, mostly in triplicateGroups 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
Multiomic analysis uncovers a continuous spectrum of differentiation and Wnt-MDK-driven immune evasion in hepatoblastomaJournal of hepatology · 2025
Summary (written by the submitters)
Background & Aims: Hepatoblastoma is the most common pediatric cancer of the liver and the majority of cases display activating mutations in the Wnt/β-catenin pathway. Understanding the complex milieu of the tumor microenvironment has resulted in promising new therapies for adult cancers, but similar approaches in pediatric cancers are still lacking.
Read the full summary ↓
We aimed to provide a comprehensive analysis of the tumor microenvironment of hepatoblastoma unveiling its spatial architecture and key signaling mechanisms. Methods: Single-cell/-nucleus RNA-seq (n=15), spatial transcriptomics (n=22), and multiplex immunofluorescence stainings (n=7) of treated, untreated, and metastasized pediatric hepatoblastomas were performed. An RNA-seq validation cohort (n=110) including hepatoblastoma, non-tumor and fetal liver samples and single-cell RNA-seq data of healthy immune cells were used for further analysis. Western blotting and RNA-seq of hepatoblastoma and macrophage cell lines were conducted for experimental validation. Results: Of four identified transcriptional tumor programs, “Developmental” and “Metabolic” reflected different hepatic differentiation stages, while “Cycling” was enriched in undifferentiated cells and relapsed samples, and “Intermediate” displayed high activity in samples from patients with poor outcomes. We discovered an increased ratio of anti- to pro-inflammatory immune cells and evidence of immune exclusion from tumor areas. Wnt-responsive upregulation of the immunomodulator midkine in hepatoblastoma cells was associated with a change in macrophage phenotype, which could be partially reversed through midkine inhibition. Conclusions: Hepatoblastoma cells exist along a continuous spectrum of hepatic differentiation and inhabit an altered immune environment. Wnt signaling augments midkine expression, which appears to be involved in shaping the immune environment by modifying macrophages to enable immune evasion, thereby providing a potential therapeutic target.
Published in
Multiomic analysis uncovers a continuous spectrum of differentiation and Wnt-MDK-driven immune evasion in hepatoblastoma
Münter D, de Faria FW, Richter M et al. · Journal of hepatology 2025 · PMID 39900120 · doi:10.1016/j.jhep.2025.01.031
This dataset
Open GSE261956 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 GSE261956_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 PRJNA1089626 and SRA study SRP496482. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39900120) ↗
Study design
17 conditions, mostly in triplicate
Huh6_ctrl ×3 Huh6_PKF_0.2µM ×3 Huh6_PKF_0.3µM ×3 Huh6_PKF_0.4µM ×3 Huh6_iMDK_5µM ×3 Huh6_iMDK_10µM ×3 HepG2_ctrl ×3 HepG2_PKF_0.2µM ×3 +10 more

Supports a between-group comparison across 51 samples.

How we read this: 17 replicated groups read from 52 sample titles; they account for 51 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

+ 12 more — browse all 52 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

Share this dataset

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.