PCOS Hub Androgen-Related Genes: Bioinformatics Analysis of 60 Samples

Xinling He published a dataset on 2026-03-19 containing results from a bioinformatics study of Polycystic Ovary Syndrome (PCOS). The data includes 60 samples (26 healthy, 34 PCOS) from five GEO datasets, used to identify four hub androgen-related genes (ALDH1A1, DHRS9, PRKCB, SGPL1). The study constructed a predictive nomogram and explored gene expression patterns and immune cell infiltration in PCOS subtypes.

Use Cases

• Validating hub gene expression (ALDH1A1, DHRS9, PRKCB, SGPL1) in PCOS models based on the identified biomarkers.
• Building predictive models for PCOS risk based on the described nomogram construction methodology.
• Analyzing immune cell infiltration patterns associated with different androgen-related gene clusters.
• Comparing gene expression profiles between healthy and PCOS samples as described in the analysis.

Strengths

• Data integrates results from five distinct GEO datasets (GSE34526, GSE80432, GSE95728, GSE124226, GSE137684).
• Analysis identified four specific hub genes (ALDH1A1, DHRS9, PRKCB, SGPL1) through three algorithmic methods (LASSO, RF, PPI).
• Includes validation of gene expression levels via RT-qPCR in PCOS mouse ovarian tissues.

Limitations

• Row count and column-level documentation are absent; field semantics must be inferred after download.
• The dataset is very small (12.3 KB), indicating limited scope, likely containing summary results rather than raw data.
• Data may reflect bias inherent to the specific GEO datasets and bioinformatics methods used.

Provenance

Source

figshare, author Xinling He

Collection Method

Bioinformatics analysis of five public GEO datasets, normalized with batch effect removal, followed by differential expression, PPI network, LASSO, and random forest algorithms.

Freshness

Last updated 2026-03-19 05:20:14; freshness should be verified.