GEO series

Lifetime ovulatory years and ovarian cancer gene expression profiles

GSE232352 Homo sapiens (human) RNA-seq (gene expression) 234 samples Submitted 2024/12/31 Platform GPL18573

At a glance

RNA-seq (gene expression) · human · 234 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: 234 samples: 234 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.
Paper: Lifetime ovulatory years and ovarian cancer gene expression profilesJournal of ovarian research · 2022

Summary (written by the submitters)

Greater ovulatory years is associated with increased ovarian cancer risk. Although ovulation leads to an acute pro-inflammatory local environment, how long-term exposure to ovulation impacts ovarian carcinogenesis is not fully understood. Thus, we examined the association between gene expression profiles of ovarian tumors and lifetime ovulatory years to enhance understanding of associated biological pathways.

Published in

Lifetime ovulatory years and ovarian cancer gene expression profiles

Sasamoto N, Stewart PA, Wang T et al. · Journal of ovarian research 2022 · PMID 35562768 · doi:10.1186/s13048-022-00995-1

This dataset

Open GSE232352 in finder → Find more human RNA-seq datasets → Ovarian cancer landscape →

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 GSE232352_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 PRJNA971658 and SRA study SRP437291. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 35562768) ↗

Study design

234 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 234 sample titles: 234 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

GSM7329079 001 RNA
GSM7329080 003 RNA
GSM7329081 004 RNA
GSM7329082 005 RNA
GSM7329083 006 RNA
GSM7329084 007 RNA
GSM7329085 008 RNA
GSM7329086 009 RNA
GSM7329087 010 RNA
GSM7329088 011 RNA
GSM7329089 012 RNA
GSM7329090 013 RNA
GSM7329091 015 RNA
GSM7329092 016 RNA
GSM7329093 017 RNA
GSM7329094 018 RNA
GSM7329095 019 RNA
GSM7329096 020 RNA
GSM7329097 022 RNA
GSM7329098 023 RNA
GSM7329099 024 RNA
GSM7329100 025 RNA
GSM7329101 026 RNA
GSM7329102 027 RNA
GSM7329103 028 RNA
GSM7329104 029 RNA
GSM7329105 030 RNA
GSM7329106 031 RNA
GSM7329107 032 RNA
GSM7329108 033 RNA
GSM7329109 034 RNA
GSM7329110 035 RNA
GSM7329111 036 RNA
GSM7329112 037 RNA
GSM7329113 038 RNA
GSM7329114 039 RNA
GSM7329115 040 RNA
GSM7329116 042 RNA
GSM7329117 043 RNA
GSM7329118 044 RNA

+ 194 more — browse all 234 samples with per-sample file links →

More Ovarian cancer datasets

GSE268514 Integrative genomic analysis of Korean high-grade serous ovarian cancer 161 samples same disease briefing · shares ovarian
GSE311212 Transcriptomic profiling in human ovarian cancer cell line OVCAR-3 following CDK2 inhibitor treatment 6 samples same disease briefing · shares ovarian
GSE128098 TET1 reprograms the epithelial ovarian cancer epigenome and reveals casein kinase 2α as a therapeutic target 4 samples same disease briefing · shares ovarian
GSE154600 Multi-omic analysis of subtype evolution and heterogeneity in high-grade serous ovarian carcinoma 5 samples same disease briefing · shares ovarian
GSE57477 Molecular subtyping of serous ovarian tumors reveals multiple connections to intrinsic breast cancer subtypes 72 samples same disease briefing · shares ovarian
GSE314300 Rb CUT&RUN profiling in ovarian cancer cell lines following CDK2 inhibitor treatment 10 samples same disease briefing · shares ovarian
GSE135242 Hi-C identifies structural variation associated with resistance in paired patient-derived ovarian cancer models 8 samples same disease briefing · shares ovarian
GSE28330 Telomeric Allelic Imbalance Indicates Defective DNA Repair and Sensitivity to DNA-Damaging Agents 107 samples same disease briefing

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.