GEO series

Dynamic change of immune microenvironment in ovarian cancer following neoadjuvant chemotherapy [scRNA-seq]

GSE303696 Mus musculus (mouse) Single-cell RNA-seq 12 samples Submitted 2025/07/30 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 12 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Mus musculus (mouse)
Samples: 12 samples: 3 × C vs 3 × CT vs 3 × P vs 3 × PTGroups 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)

Initial surgery and platinum-based chemotherapy is extensively applied to ovarian cancer. Although the majority of patients were sensitive, most relapse at later stage, highlighting the urgent need to understand the tumor microenvironment in ovarian cancer following NACT (neoadjuvant chemotherapy). Through analyzing the published scRNA-seq on human high-grade serous ovarian cancer and performing several in vitro and in vivo experiments, we found prostaglandins-mediated immunosuppressive microenvironment formation is a critical contributor to chemotherapy-resistant upon cisplatin treatment.

Read the full summary ↓

In mechanisms, cisplatin-treated ovarian cancer cells induce the formation of MDSCs, which passivates the cytotoxicity of CD8+T cell. Moreover, we also carried out scRNA-seq on mice ovarian cancer intraperitoneal injection model to confirm our findings. Notably, Collaborative application of cisplatin and prostaglandins-specific inhibition boosts the cytotoxicity of CD8+T cell and greatly improves the effective rate compared to cisplatin monotherapy.

This dataset

Open GSE303696 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 GSE303696_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1297110 and SRA study SRP603719. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

3 × C vs 3 × CT vs 3 × P vs 3 × PT

Supports a between-group comparison across 12 samples.

How we read this: 4 replicated groups read from 12 sample titles; they account for 12 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

GSM9134244 C1 C
GSM9134245 C2 C
GSM9134246 C3 C
GSM9134247 CT1 CT
GSM9134248 CT2 CT
GSM9134249 CT3 CT
GSM9134250 P1 P
GSM9134251 P2 P
GSM9134252 P3 P
GSM9134253 PT1 PT
GSM9134254 PT2 PT
GSM9134255 PT3 PT

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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.