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Combinatorial Immunotherapeutic Strategies in Liver Cancer

GSE296090 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2025/08/27 Platform GPL34328
At a glance

RNA-seq (gene expression) · mouse · 18 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
Mus musculus (mouse)
Samples
18 samples: 18 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
PolyIC as an adjuvant outperforms anti-VEGF in combination with anti-PD-L1 therapy in mouse liver tumor modelsHepatology communications · 2025
Summary (written by the submitters)
Background: Immune checkpoint inhibitors combined with antiangiogenic therapy has become the standard of care for advanced hepatocellular carcinoma, albeit with limited therapeutic benefit. Our previous studies demonstrated the immunomodulatory and antitumor effects of polyIC, a synthetic double-stranded RNA.
Read the full summary ↓
Here, we compared the efficacy of anti-programmed death ligand 1 (?PD-L1) plus polyIC versus ?PD-L1 plus anti-vascular endothelial growth factor (?VEGF) in primary liver tumor models.Conclusion: This preclinical study identifies polyIC as a better enhancer of ?PD-L1 efficacy than ?VEGF in liver cancer, providing a novel strategy for improving immunotherapy outcomes. Further clinical investigation is warranted.
Published in
PolyIC as an adjuvant outperforms anti-VEGF in combination with anti-PD-L1 therapy in mouse liver tumor models
Ji Y, Lu LC, Zhuang H et al. · Hepatology communications 2025 · PMID 40824253 · doi:10.1097/HC9.0000000000000776
This dataset
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.

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 GSE296090_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1257769 and SRA study SRP582563. Searching any of these in the dataset finder brings you back here.

Study design
18 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 18 sample titles: 18 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
Datasets on a similar topic

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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.