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Dual Programmed Death Receptor-1 and Vascular Endothelial Growth Factor Receptor-2 Blockade Promotes Vascular Normalization and Enhances Antitumor Immune Responses in Hepatocellular Carcinoma

GSE301140 Mus musculus (mouse) RNA-seq (gene expression) 18 samples Submitted 2025/10/26 Platform GPL24247
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: 5 × combination_aCD4, vs 5 × combination, vs 4 × control, vs 4 × aPD1,Groups 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
Inhibiting B cells enhances the efficacy of STING agonism or immune checkpoint blockade in hepatocellular carcinomaNature communications · 2025
Summary (written by the submitters)
Background and aims: Activation of the antitumor immune response using programmed death receptor-1 (PD-1) blockade showed benefit only in a fraction of patients with hepatocellular carcinoma (HCC). Combining PD-1 blockade with antiangiogenesis has shown promise in substantially increasing the fraction of patients with HCC who respond to treatment, but the mechanism of this interaction is unknown.
Read the full summary ↓
Approach and results: We recapitulated these clinical outcomes using orthotopic-grafted or induced-murine models of HCC. Specific blockade of vascular endothelial receptor 2 (VEGFR-2) using a murine antibody significantly delayed primary tumor growth but failed to prolong survival, while anti-PD-1 antibody treatment alone conferred a minor survival advantage in one model. However, dual anti-PD-1/VEGFR-2 therapy significantly inhibited primary tumor growth and doubled survival in both models. Combination therapy reprogrammed the immune microenvironment by increasing cluster of differentiation 8-positive (CD8+ ) cytotoxic T cell infiltration and activation, shifting the M1/M2 ratio of tumor-associated macrophages and reducing T regulatory cell (Treg) and chemokine (C-C motif) receptor 2-positive monocyte infiltration in HCC tissue. In these models, VEGFR-2 was selectively expressed in tumor endothelial cells. Using spheroid cultures of HCC tissue, we found that PD-ligand 1 expression in HCC cells was induced in a paracrine manner upon anti-VEGFR-2 blockade in endothelial cells in part through interferon-gamma expression. Moreover, we found that VEGFR-2 blockade increased PD-1 expression in tumor-infiltrating CD4+ cells. We also found that under anti-PD-1 therapy, CD4+ cells promote normalized vessel formation in the face of antiangiogenic therapy with anti-VEGFR-2 antibody. Conclusions: We show that dual anti-PD-1/VEGFR-2 therapy has a durable vessel fortification effect in HCC and can overcome treatment resistance to either treatment alone and increase overall survival in both anti-PD-1 therapy-resistant and anti-PD-1 therapy-responsive HCC models.
Published in
Inhibiting B cells enhances the efficacy of STING agonism or immune checkpoint blockade in hepatocellular carcinoma
Liu X, Liu Z, Zhu C et al. · Nature communications 2025 · PMID 41360802 · doi:10.1038/s41467-025-66581-3
This dataset
Open GSE301140 in finder → Find more mouse RNA-seq datasets →
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 GSE301140_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 PRJNA1284180 and SRA study SRP596244. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41360802) ↗
Study design
5 × combination_aCD4, vs 5 × combination, vs 4 × control, vs 4 × aPD1,

Supports a between-group comparison across 18 samples.

How we read this: 4 replicated groups read from 18 sample titles; they account for 18 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
Datasets on a similar topic

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