← BioTransfer GEO Dataset Finder
GEO series

Senescence of tumor-specific Non-Classic Monocytes Facilitates Immune Suppression and Prostate Cancer Progression through Tumor and Host Immune Interactions

GSE288749 Mus musculus (mouse) RNA-seq (gene expression) 12 samples Submitted 2025/10/31 Platform GPL17021Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 12 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
12 samples
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)
Prostate cancer remains a leading cause of cancer-related mortality in men, with metastatic cases presenting significant therapeutic challenges due to genetic alterations that drive disease progression and immune evasion. While localized prostate cancer generally has favorable outcomes, metastatic disease is associated with much poorer survival rates.
Read the full summary ↓
This study investigates the role of specific tumor-associated senescent non-classical monocytes (NC-Mo) in MYC amplification and PTEN deletion-mediated lethal prostate cancer progression, focusing on tumor-host immune interactions. Key genetic alterations, such as MYC amplification and PTEN deletion, lead to tumor cell apoptosis via mitochondrial Complex I dysfunction, driven by metabolic reprogramming characterized by increased glycolysis and arginine biosynthesis. A specific subtype of Ly6cLowCD43LowCXCR2+ NC-Mo is activated by the phagocytosis of apoptotic tumor cells and damage-associated molecular patterns (DAMPs) through TLR4 activation, promoting their differentiation into a senescent phenotype.TLR4 activation further induces NC-Mo to secrete TNF-α, which stimulates intra-tumor endothelial cells to produce G-CSF, circulating to the bone marrow and promoting the transition of classical monocytes into NC-Mo. Once senescent, NC-Mo secrete more TGF-β1, which enhances CXCL5 secretion from tumor cells, mainly via the AKT/PI3K pathway, creating a feedback loop that recruits more peripheral NC-Mo into the tumor microenvironment (TME). Additionally, IL-18 secreted by senescent NC-Mo suppresses T cells and NK cells, weakening both adaptive and innate immune responses in the TME. Targeting NC-Mo and key pathways, such as TLR4, CXCL5-CXCR2, and IL-18-IL18R, reduce immune suppression and inhibit tumor progression. These findings provide insights into how MYC/PTEN dysfunction accelerates prostate cancer progression through specific tumor-associated senescent NC-Mo, offering novel therapeutic opportunities.
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 GSE288749_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 PRJNA1219190 and SRA study SRP561549. Searching any of these in the dataset finder brings you back here.

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 12 more — browse all 12 samples with per-sample file links →

Datasets on a similar topic

Search all mouse 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.