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Functional dissection of ovarian aging-associated genes NDUFS5 and UBE2L3 through integrated genomic analyses and validation

GSE288033 Homo sapiens (human) RNA-seq (gene expression) 62 samples Submitted 2026/01/23 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 62 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)
Samples
62 samples: 32 × POI, vs 30 × Control,Groups 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)
Ovarian aging, culminating in menopause, involves a gradual decline in ovarian function. Genetic factors influencing age at natural menopause (ANM) especially premature ovarian insufficiency (POI) warrant further research. We employed a Bayesian framework to integrate a genome-wide association study (GWAS) on ANM (N=69,360) with an expression quantitative trait loci (eQTL) dataset (N=1,490), identifying 28 significant risk genes.
Read the full summary ↓
Peripheral blood analysis from patients with POI and healthy controls, combined with RNA sequencing, differential gene (DEGs) expression analysis, and RT-qPCR verification, pinpointed MSH5, GAB2, NDUFS5, LST1, and UBE2L3 as top DEGs. NDUFS5 and UBE2L3 were aberrantly expressed in hydrogen peroxide or cyclophosphamide-treated human granulosa cells. Immunohistochemisty revealed decreased NDUFS5 and increased UBE2L3 expression in human ovarian samples of varying ages. NDUFS5 knockdown reduced granulosa cell proliferation and increased apoptosis in cell culture; UBE2L3 knockdown exhibited the opposite effect. Together, our findings indicate NDUFS5 and UBE2L3 as key risk genes involved in ovarian aging.
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 GSE288033_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 PRJNA1215751 and SRA study SRP559910. Searching any of these in the dataset finder brings you back here.

Study design
32 × POI, vs 30 × Control,

Supports a between-group comparison across 62 samples.

How we read this: 2 replicated groups read from 62 sample titles; they account for 62 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

+ 22 more — browse all 62 samples with per-sample file links →

Datasets on a similar topic

Search all human 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.