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Alternative splicing- and gene fusion-derived neoepitopes in MASLD-associated hepatocellular carcinoma

GSE334651 Homo sapiens (human) RNA-seq (gene expression) 120 samples Submitted 2026/06/10 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 120 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)
Topic
Hepatocellular carcinomaThis series is part of our curated briefing on it.
Samples
120 samples: 95 × MASLD-HCC vs 13 × MASLD vs 12 × 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.
Paper
FOXM1 influences DNA methylation to augment TACC3 alternative splicing directed by KAT2A in hepatocellular carcinomaClinical and molecular hepatology · 2026
Summary (written by the submitters)
Metabolic dysfunction-associated steatotic liver disease (MASLD)-driven hepatocellular carcinoma (HCC) exhibits reduced responsiveness to immune checkpoint blockade compared with viral or other etiologies, suggesting underlying differences in tumor immunogenicity and immune evasion.
Read the full summary ↓
To characterize the immunogenic landscape of MASLD-driven HCC, we profiled transcriptomic data from 120 liver tissue samples, including healthy controls (n = 12), MASLD samples (n = 13), and MASLD-associated HCC samples (n = 95), and investigated the contribution of transcriptome-derived neoantigens to this altered immune landscape, focusing on alternative splicing and gene fusion events as non-canonical sources of tumor-specific epitopes.
Published in
FOXM1 influences DNA methylation to augment TACC3 alternative splicing directed by KAT2A in hepatocellular carcinoma
Zhao LN, Andersen JB · Clinical and molecular hepatology 2026 · PMID 41592600 · doi:10.3350/cmh.2025.1370
This dataset
Open GSE334651 in finder → Find more human RNA-seq datasets → Hepatocellular carcinoma landscape →
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 GSE334651_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 PRJNA1467981. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41592600) ↗
Study design
95 × MASLD-HCC vs 13 × MASLD vs 12 × CTRL

Supports a between-group comparison across 120 samples.

How we read this: 3 replicated groups read from 120 sample titles; they account for 120 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

+ 80 more — browse all 120 samples with per-sample file links →

More Hepatocellular carcinoma datasets

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.