GEO series

Multi-omics analysis of liver molecular dysregulation leading to non-viral related hepatocellular carcinoma development

GSE244826 Homo sapiens (human) RNA-seq (gene expression) 69 samples Submitted 2025/03/02 Platform GPL28038

At a glance

RNA-seq (gene expression) · human · 69 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: 69 samples
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: Multiomics Analysis of Liver Molecular Dysregulation Leading to Nonviral-Related Hepatocellular Carcinoma DevelopmentJournal of proteome research · 2025

Summary (written by the submitters)

Chronic liver diseases exhibit diverse backgrounds, and it is believed that numerous factors contribute to progression to cancer. To achieve effective prevention of non-viral hepatocellular carcinoma, it is imperative to identify fundamental molecular abnormalities at the patient level. Utilizing cancer-adjacent liver tissues obtained from hepatocellular carcinoma patients (chronic liver disease), we conducted RNA-Seq and metabolome analyses.

Read the full summary ↓

In the chronic liver disease cohort, upregulation of inflammation-associated signals was observed, concomitant with accumulation of acylcarnitine and fatty acid and depletion of NADP+, gamma-tocopherol, and dehydroisoandrosterone-3-sulfate-1 (DHEAS). To minimize heterogeneity, we performed multi-omics clustering, successfully categorizing the chronic liver disease cases into two distinct subtypes. Subtype 1 demonstrated elevated inflammatory levels, whereas Subtype 2 included a disproportionately high proportion of elderly cases. Furthermore, RNA-Seq analysis revealed upregulation of inflammatory signals in Subtype 1, while both subtypes exhibited downregulation of fatty acid metabolism. Metabolome analysis indicated a tendency of increased acylcarnitine levels in Subtype 1 and augmented fatty acid accumulation in Subtype 2. Validation of differentially expressed genes using the Gene Expression Omnibus (GEO) dataset revealed the potential for amelioration through supplementation with antioxidants such as epigallocatechin gallate (ECGC).

Published in

Multiomics Analysis of Liver Molecular Dysregulation Leading to Nonviral-Related Hepatocellular Carcinoma Development

Nakahara H, Ono A, Hayes CN et al. · Journal of proteome research 2025 · PMID 39982271 · doi:10.1021/acs.jproteome.4c00729

This dataset

Open GSE244826 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 GSE244826_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 PRJNA1024967 and SRA study SRP464915. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39982271) ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 69 more — browse all 69 samples with per-sample file links →

More Hepatocellular carcinoma datasets

GSE105130 Comprehensive analysis of Transcriptome Profiles in Hepatocellular Carcinoma patients 52 samples same disease briefing · shares hepatocellular
GSE140228 Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma 41 samples same disease briefing · shares hepatocellular
GSE14520 Gene expression data of human hepatocellular carcinoma (HCC) 488 samples same disease briefing · shares hepatocellular
GSE10143 Gene Expression in Fixed Tissues and Outcome in Hepatocellular Carcinoma 387 samples same disease briefing · shares hepatocellular
GSE107170 Molecular Signature of Hepatitis D Virus-Associated Hepatocellular Carcinoma 307 samples same disease briefing · shares hepatocellular
GSE104580 Using a novel gene signature for predicting the efficacy of the transarterial chemoembolization in patients with hepatocellular carcinoma. 147 samples same disease briefing · shares hepatocellular
GSE109211 Molecular predictors of prevention of recurrence in hepatocellular carcinoma with sorafenib as adjuvant treatment in the phase 3 STORM trial 140 samples same disease briefing · shares hepatocellular
GSE10186 Integrative Transcriptome Analysis Reveals Common Molecular Subtypes of Human Hepatocellular Carcinoma 118 samples same disease briefing · shares hepatocellular

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.