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Dual Targeting of STING and PI3Kγ Eliminates Regulatory B Cells to Overcome STING Resistance for Pancreatic Cancer Immunotherapy [RNA-seq]

GSE275780 Mus musculus (mouse) RNA-seq (gene expression) 17 samples Submitted 2026/02/20 Platform GPL34290
At a glance

RNA-seq (gene expression) · mouse · 17 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
17 samples: 17 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)
The immune suppression in tumors and lymph nodes of pancreatic ductal adenocarcinoma (PDAC), regulated by suppressive myeloid cells and regulatory B (Breg) cells, hinders the effectiveness of immunotherapy. Although STING agonists activate myeloid cells to overcome immune suppression, it expands Breg cells, conferring STING resistance in PDAC.
Read the full summary ↓
We discovered that blocking PI3Kγ during STING activation abolished IRF3 phosphorylation to eliminate Breg cells, while PI3Kγ inhibition sustained STING-induced IRF3 phosphorylation to preserve STING function in myeloid cells. Therefore, we developed a dual functional compound SH-273 and its albumin nanoformulation Nano-273, which stimulates STING to activate myeloid cells and inhibits PI3Kγ to eliminates Breg cells overcoming STING resistance. Nano-273 achieved systemic antitumor immunity through intravenous administration, which decreases Breg cells and remodels microenvironment in tumors and lymph nodes. Nano-273, combined with anti-PD-1, extended median survival to 200 days in transgenic KPC PDAC mice (KrasG12D-P53R172H-Cre), offering potential for PDAC treatment.
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 GSE275780_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 PRJNA1152981 and SRA study SRP528940. Searching any of these in the dataset finder brings you back here.

Study design
17 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 11 samples read as cases, 6 as controls.

How we read this: Read from 17 sample titles: 17 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
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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.