Dataset Information

Gene expression data from normal endometrium (NE), endometrium with endometriosis (EE) and ovarian endometrioma (OE)

ABSTRACT: This study aimed to identify an essential gene in NE, EE and OE.

ORGANISM(S): Homo sapiens

PROVIDER: GSE201912 | GEO | 2022/05/02

REPOSITORIES: GEO

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Project description:BACKGROUND: Endometriosis is a common benign gynaecological disorder. A number of studies have confirmed that the occurrence of disease in endometriosis patients is associated with metabolic changes in biological fluids and tissues. We set out to investigate the metabolic characteristics of follicular fluid in patients with ovarian endometriosis undergoing in vitro fertilization (IVF).METHODS: The current research project is an exploratory cohort study on endometriosis. A total of 19 infertile patients with ovarian endometriosis diagnosed by laparoscopy were enrolled in this study and 23 age- and BMI-matched controls (women with infertility due to male or tubal factors). All the patients underwent IVF treatment with a gonadotropin-releasing hormone antagonist protocol and completed follicular fluid (FF) collection at oocyte retrieval. The metabolomics of FF samples was analyzed by ultra high performance liquid chromatography-orbitrap exploris-mass spectrometer (UHPLC-OE-MS). The best combination of biomarkers was then selected by performing stepwise logistic regression analysis with backward elimination. RESULTS: Fifteen metabolites were identified as biomarkers associated with endometriosis. A final model containing 8-hydroxy-2-deoxyguanosine, biotin, n-acetyl-L-methionine and n-methylnicotinamide was constructed. Receiver operating characteristic analysis confirmed the value of these parameters in diagnosing endometriosis, with a sensitivity of 94.7% and a specificity of 95.7%. Analysis of fortification via the Kyoto Encyclopedia of Genes and Genome showed that 15 metabolites were enriched in 8 metabolic pathways.CONCLUSION: Metabolomics based on UHPLC-OE-MS effectively characterized the metabolomics analysis of FF in patients with ovarian endometriosis. These findings may provide a new basis for a better understanding of how diseases progress and for the discovery of new biomarkers.

Dataset Information

Gene expression data from normal endometrium (NE), endometrium with endometriosis (EE) and ovarian endometrioma (OE)

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