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Time-resolved transcriptomic profiling of surgical wounds identifies stage-specific therapeutic targets for residual ovarian cancer

GSE325548 Mus musculus (mouse) RNA-seq (gene expression) 48 samples Submitted 2026/03/27 Platform GPL9185
At a glance

RNA-seq (gene expression) · mouse · 48 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
48 samples: 48 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)
Background: The optimal timing of adjuvant chemotherapy after cytoreductive surgery in epithelial ovarian cancer remains uncertain, and perioperative wound-healing responses may transiently create a pro-tumorigenic and drug-resistant microenvironment. This study aimed to characterize time-dependent wound-induced transcriptomic alterations and to identify pharmacologic agents capable of reversing these responses.
Read the full summary ↓
Methods: An ID8 murine ovarian cancer model was used to compare no treatment, anesthesia alone, and anesthesia plus surgical wounding mimicking futile laparotomy. Tumors were collected at baseline, 1 day (T1), 1 week (T2), and 2 weeks (T3) after intervention. RNA sequencing was performed, and wound-specific differentially expressed genes (WsDEGs) were defined by excluding anesthesia- and progression-related signatures. Functional enrichment analyses were conducted, followed by transcriptome-based drug repurposing using the REMEDY platform to identify compounds predicted to reverse wound-induced gene expression profiles. Results: Surgical wounding significantly increased tumor burden at T1. Transcriptomic analyses revealed distinct, time-dependent wound-associated programs. At T1, WsDEGs were enriched in inflammatory signaling, coagulation, angiogenesis, and immune cell migration, with Vorinostat and Homohar-ringtonine identified as top candidates to counteract these signatures. At T2, pathways related to cell survival, adhesion, and morphogenesis predominated, with LY-2090314, Artesunate, and Birinapant emerging as potential modulators. At T3, cell-cycle regulation and lipid metabolic pathways were dominant, and Fulvestrant, Atorvastatin, Imatinib, and ABT-737 were predicted to inhibit these processes. Conclusions: Perioperative sur-gical wounding induces dynamic, stage-specific transcriptomic programs that may promote ovarian cancer progression and alter drug responsiveness. These findings support time-adapted perioperative pharmacologic strategies to optimize postoperative cancer therapy.
This dataset
Open GSE325548 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 GSE325548_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 PRJNA1440454 and SRA study SRP685464. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
48 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 48 sample titles: 48 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

+ 8 more — browse all 48 samples with per-sample file links →

Datasets on a similar topic

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.