← BioTransfer GEO Dataset Finder
GEO series

Next Generation Sequencing Facilitates Quantitative Analysis of 77-39 in ovarian cancer Transcriptomes

GSE293585 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2025/05/01 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 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
Homo sapiens (human)
Topic
Ovarian cancerThis series is part of our curated briefing on it.
Samples
12 samples: 3 × KGN cells,control vs 3 × COV434 cells,controlGroups 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.
Paper
Inhibition of SIRT3 by a specific inhibitor induces cellular senescence and growth arrest of ovarian granulosa cell tumor via p53 and NF-κB axisFrontiers in pharmacology · 2025
Summary (written by the submitters)
Next-generation sequencing (NGS) has revolutionized systems-based analysis of cellular pathways. The goals of this study are to compareExamining the role of sirt3 inhibitor 77-39 in ovarian granulosa cancer cells,kgn cell line,cov434 cell line by RNA-seq
Published in
Inhibition of SIRT3 by a specific inhibitor induces cellular senescence and growth arrest of ovarian granulosa cell tumor via p53 and NF-κB axis
Ma J, Lin S, Zeng C et al. · Frontiers in pharmacology 2025 · PMID 40777993 · doi:10.3389/fphar.2025.1608156
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 GSE293585_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 PRJNA1245298 and SRA study SRP576127. Searching any of these in the dataset finder brings you back here.

Study design
3 × KGN cells,control vs 3 × COV434 cells,control

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

How we read this: 2 replicated groups read from 12 sample titles; they account for 6 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
More Ovarian cancer datasets

Search all human 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.