Dataset Information
Screening and verification of genes related to polycystic ovary syndrome.
Ontology highlight
ABSTRACT: Objective
To identify key genes involved in occurrence and development of polycystic ovary syndrome (PCOS).Methods
By downloading the GSE85932 dataset from the GEO database, we used bioinformatical analysis to analyse differentially expressed genes (DEGs) from blood samples of eight women with PCOS and eight matched controls. Following bioinformatic analysis, we performed a cross-sectional study of serum samples taken from 79 women with PCOS and 36 healthy controls.Results
From the 178 DEGs identified by bioinformatical analysis, 15 genes were identified as significant, and of these, ORM1 and ORM2 were selected for further verification as potential biomarkers for PCOS. Serum ORM1 and ORM2 levels were significantly increased in women with PCOS, and had a high diagnostic value. ORM1 and ORM2 were positively correlated with testosterone, cholesterol, and triglycerides. ORM1 levels were negatively correlated with high density lipoprotein (HDL) while ORM2 levels showed no significant correlation.Conclusions
ORM may be an effective biomarker for the diagnosis of PCOS and its monitoring may be a useful therapeutic strategy.
SUBMITTER: Li X
PROVIDER: S-EPMC9837284 | biostudies-literature | 2023 Jan
REPOSITORIES: biostudies-literature
Publications
Screening and verification of genes related to polycystic ovary syndrome.
The Journal of international medical research 20230101 1
<h4>Objective</h4>To identify key genes involved in occurrence and development of polycystic ovary syndrome (PCOS).<h4>Methods</h4>By downloading the GSE85932 dataset from the GEO database, we used bioinformatical analysis to analyse differentially expressed genes (DEGs) from blood samples of eight women with PCOS and eight matched controls. Following bioinformatic analysis, we performed a cross-sectional study of serum samples taken from 79 women with PCOS and 36 healthy controls.<h4>Results</h ...[more]