GEO series

Remodeling of Suppressive Melanoma Microenvironments by Particle-Mediated Modulation of the STING/IL-6/PD-L1 Axis Improves Tumor Immunity and Survival

GSE310954 Mus musculus (mouse) RNA-seq (gene expression) 11 samples Submitted 2025/12/29 Platform GPL24247

At a glance

RNA-seq (gene expression) · mouse · 11 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: 11 samples: 3 × B16_GM_CT vs 3 × B16_GM_CDOT vs 3 × B16_F10_CDOT vs 2 × B16_F10_CTGroups 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)

Despite the considerable success of clinically approved immune-based therapies for treating advanced melanoma, a significant fraction of these patients are not responsive due to mechanisms engaged by the tumor to evade the immune system.

Read the full summary ↓

Here we report the surprising finding that a clinically validated and tunable self-therapeutic ultrasmall silica nanoparticle prolongs survival in a highly resistant melanoma model in combination with IL-6 and PD-L1 inhibition through its activation of the STING/IL-6/PD-L1 axis and reprogramming of the tumor microenvironment (TME) towards a proinflammatory phenotype. Induction of significant cytotoxic and anti-tumor inflammatory responses lead to differential activation of immune cell populations in a model- and CD8-dependent manner via Type I/II interferon pathways after systemic particle injection. Importantly, these immunostimulatory responses accompany significant reductions in cell populations and receptors driving suppressive activities. Mechanistic insights highlight the potential clinical utility of this platform to maximize anti-tumor immunity and efficacy by subverting suppressive components in the TME.

This dataset

Open GSE310954 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 GSE310954_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 PRJNA1367593 and SRA study SRP648299. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

3 × B16_GM_CT vs 3 × B16_GM_CDOT vs 3 × B16_F10_CDOT vs 2 × B16_F10_CT

Supports a between-group comparison across 11 samples.

How we read this: 4 replicated groups read from 11 sample titles; they account for 11 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

GSM9313094 B16_GM_CT_1 B16_GM_CT
GSM9313095 B16_GM_CT_2 B16_GM_CT
GSM9313096 B16_GM_CT_3 B16_GM_CT
GSM9313097 B16_GM_CDOT_1 B16_GM_CDOT
GSM9313098 B16_GM_CDOT_2 B16_GM_CDOT
GSM9313099 B16_GM_CDOT_3 B16_GM_CDOT
GSM9313100 B16_F10_CT_1 B16_F10_CT
GSM9313101 B16_F10_CT_2 B16_F10_CT
GSM9313102 B16_F10_CDOT_1 B16_F10_CDOT
GSM9313103 B16_F10_CDOT_2 B16_F10_CDOT
GSM9313104 B16_F10_CDOT_3 B16_F10_CDOT

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Search all mouse RNA-seq datasets in GEO →

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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.