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Gene community-regulated pathway analysis successfully deciphers pathogenesis and biomarkers of viral versus metabolic hepatocellular carcinoma

GSE272035 Homo sapiens (human) RNA-seq (gene expression) 71 samples Submitted 2025/07/10 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 71 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
71 samples: 71 samples, each with a distinct title — no replicated groupsGroups 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.
Summary (written by the submitters)
Hepatocellular carcinoma (HCC) is a leading cause of cancer deaths globally. Understanding its mechanisms is crucial, despite advancements in gene identification. To address the variability of transcriptome data across cohorts and assess the impact of single gene on pathways, we developed a Gene community-Regulated Pathways (GReP) method with an Integrated Gene Community score.
Read the full summary ↓
Using The Cancer Genome Atlas and the Taiwan Liver Cancer Network cohorts, GReP identified 531 consensus genes and 137 pathways across four categories: central dogma, metabolism, immune, and metastasis. GReP then identified potential biomarkers and categorized patients into metabolic HCC (mostly males with alcoholism and NAFLD) and viral HCC (prone to cirrhosis and HBV/HCV infection) groups, linking genotypes (gene categories) and phenotypes (clinical data) to uncover distinct mechanisms. This approach can be expanded across diverse cohorts to expedite the discovery of promising targets and therapeutic interventions for various diseases.
This dataset
Open GSE272035 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 GSE272035_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 PRJNA1134752 and SRA study SRP519418. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
71 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 71 sample titles: 71 distinct titles with little repetition. 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

+ 31 more — browse all 71 samples with per-sample file links →

More Hepatocellular carcinoma datasets

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.