Dataset Information

Lipidomic Biomarkers in Polycystic Ovary Syndrome and Endometrial Cancer.

ABSTRACT: Women with polycystic ovary syndrome (PCOS) are more likely to develop endometrial cancer (EC). The molecular mechanisms which increase the risk of EC in PCOS are unclear. Derangements in lipid metabolism are associated with EC, but there have been no studies, investigating if this might increase the risk of EC in PCOS. This was a cross-sectional study of 102 women in three groups of 34 (PCOS, EC and controls) at Nottingham University Hospital, UK. All participants had clinical assessments, followed by obtaining plasma and endometrial tissue samples. Lipidomic analyses were performed using liquid chromatography (LC) coupled with high resolution mass spectrometry (HRMS) and the obtained lipid datasets were screened using standard software and databases. Using multivariate data analysis, there were no common markers found for EC and PCOS. However, on univariate analyses, both PCOS and EC endometrial tissue samples showed a significant decrease in monoacylglycerol 24:0 and capric acid compared to controls. Further studies are required to validate these findings and investigate the potential role of monoacylglycerol 24:0 and capric acid in the link between PCOS with EC.

SUBMITTER: Shafiee MN

PROVIDER: S-EPMC7370092 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Lipidomic Biomarkers in Polycystic Ovary Syndrome and Endometrial Cancer.

Shafiee Mohamad Nasir MN Ortori Catharine A CA Barrett David A DA Mongan Nigel P NP Abu Jafaru J Atiomo William W

International journal of molecular sciences 20200703 13

Women with polycystic ovary syndrome (PCOS) are more likely to develop endometrial cancer (EC). The molecular mechanisms which increase the risk of EC in PCOS are unclear. Derangements in lipid metabolism are associated with EC, but there have been no studies, investigating if this might increase the risk of EC in PCOS. This was a cross-sectional study of 102 women in three groups of 34 (PCOS, EC and controls) at Nottingham University Hospital, UK. All participants had clinical assessments, foll ...[more]

PMID: 32635401

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Project description:BackgroundIn this study, we aimed to identify novel biomarkers for polycystic ovary syndrome (PCOS) and analyze their potential roles in immune infiltration during PCOS pathogenesis.MethodsFive datasets, namely GSE137684, GSE80432, GSE114419, GSE138518, and GSE155489, were obtained from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were selected from the train datasets. The least absolute shrinkage and selection operator logistic regression model and support vector machine-recursive feature elimination algorithm were combined to screen potential biomarkers. The test datasets validated the expression levels of these biomarkers, and the area under the curve (AUC) was calculated to analyze their diagnostic value. Quantitative real-time PCR was conducted to verify biomarkers' expression in clinical samples. CIBERSORT was used to assess differential immune infiltration, and the correlations of biomarkers with infiltrating immune cells were evaluated.ResultsHerein, 1265 DEGs were identified between PCOS and control groups. The gene sets related to immune response and adaptive immune response were differentially activated in PCOS. The two diagnostic biomarkers of PCOS identified by us were HD domain containing 3 (HDDC3) and syndecan 2 (SDC2; AUC, 0.918 and 0.816, respectively). The validation of hub biomarkers in clinical samples using RT-qPCR was consistent with bioinformatics results. Immune infiltration analysis indicated that decreased activated mast cells (P = 0.033) and increased eosinophils (P = 0.040) may be a part of the pathogenesis of PCOS. HDDC3 was positively correlated with T regulatory cells (P = 0.0064), activated mast cells (P = 0.014), and monocytes (P = 0.024) but negatively correlated with activated memory CD4 T cells (P = 0.016) in PCOS. In addition, SDC2 was positively correlated with activated mast cells (P = 0.0021), plasma cells (P = 0.0051), and M2 macrophages (P = 0.038) but negatively correlated with eosinophils (P = 0.01) and neutrophils (P = 0.031) in PCOS.ConclusionHDDC3 and SDC2 can serve as candidate biomarkers of PCOS and provide new insights into the molecular mechanisms of immune regulation in PCOS.

Dataset Information

Lipidomic Biomarkers in Polycystic Ovary Syndrome and Endometrial Cancer.

Publications

Lipidomic Biomarkers in Polycystic Ovary Syndrome and Endometrial Cancer.

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