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Transcriptome profile of hepatocellular carcinoma in patients with atezolizumab plus bevacizumab therapy.

GSE285963 Homo sapiens (human) RNA-seq (gene expression) 84 samples Submitted 2025/10/02 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 84 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
84 samples: 84 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.
Paper
The efficacy of atezolizumab plus bevacizumab for advanced hepatocellular carcinoma in relation to tumor-infiltrating lymphocytesJHEP reports : innovation in hepatology · 2025
Summary (written by the submitters)
Hepatocellular carcinoma (HCC) is a rapidly increasing and highly lethal cancer worldwide, with many patients diagnosed at advanced stages and early-stage patients often experiencing high recurrence rates. The combination of atezolizumab plus bevacizumab emerged as a groundbreaking immunotherapy, establishing a new standard of care for advanced HCC.
Read the full summary ↓
There's an urgent need for reliable biomarkers to predict patient responses and personalize treatment. We collected tumor samples from advanced HCC patients wirth atezolizumab plus bevacizumab therapy and performed comprehensive analyses of the tumor microenvironment using bulk RNA sequencing to identify key features influencing the effectiveness of atezolizumab plus bevacizumab.
Published in
The efficacy of atezolizumab plus bevacizumab for advanced hepatocellular carcinoma in relation to tumor-infiltrating lymphocytes
Kanzaki H, Ishino T, Ogasawara S et al. · JHEP reports : innovation in hepatology 2025 · PMID 41362708 · doi:10.1016/j.jhepr.2025.101614
This dataset
Open GSE285963 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 GSE285963_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 PRJNA1207067 and SRA study SRP555581. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41362708) ↗
Study design
84 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 84 sample titles: 84 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

+ 44 more — browse all 84 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.