Genetic regulation of disease risk and endometrial gene expression highlights potential target genes for endometriosis and polycystic ovarian syndrome

Fung, Jenny N.; Mortlock, Sally; Girling, Jane E.; Holdsworth-Carson, Sarah J.; Teh, Wan Tinn; Zhu, Zhihong; Lukowski, Samuel W.; McKinnon, Brett; McRae, Allan F.; Yang, Jian; Healey, Martin; Powell, Joseph E.; Rogers, Peter A. W.; Montgomery, Grant W.

doi:10.1038/s41598-018-29462-y

Cited by 47 publications

(78 citation statements)

References 79 publications

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“…Whereas some genes exhibit more tissue-specific effects, others have similar effects in a range of tissues (20,61,62). We assessed colocalization using datasets including a wide range of tissue types (e.g.…”

Section: Discussionmentioning

confidence: 99%

Colocalization highlights genes in hypothalamic–pituitary–gonadal axis as potentially mediating polycystic ovary syndrome risk

Censin

Bovijn

Holmes

et al. 2020

Preprint

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Polycystic ovary syndrome (PCOS) is a common disease in women with consequences for reproductive, metabolic and psychological health. Women with PCOS have disrupted signalling in the hypothalamic-pituitary-gonadal axis and studies have indicated that the disease has a large genetic component. While a recent genome-wide association study of PCOS performed in up to 10,074 cases and 103,164 controls of European decent identified 14 PCOS-associated regions, much of the disease pathophysiology remains unclear.Here, we use a Bayesian colocalization approach to highlight genes that may have a potential role in PCOS pathophysiology and thus are of particular interest for further functional follow-up. We evaluated the posterior probabilities of shared causal variants between PCOS genetic risk loci and intermediate cellular phenotypes in one protein and two expression quantitative trait locus datasets, respectively. Sample sizes ranged from 80 to 31,684. In total, we identified seven proteins or genes with evidence of a shared causal variant for almost a third of PCOS signals, including follicle stimulating hormone (FSH) and the genes ERBB3, IKZF4, RPS26, SUOX, ZFP36L2, and C8orf49. Several of these genes and proteins have been implicated in the hypothalamic-pituitary-gonadal signalling pathway.In summary, our results suggest potential effector proteins and genes for PCOS association signals. This highlights genes for functional follow-up in order to demonstrate a causal role in PCOS pathophysiology.

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Section: Discussionmentioning

confidence: 99%

Colocalization highlights genes in hypothalamic–pituitary–gonadal axis as potentially mediating polycystic ovary syndrome risk

Censin

Bovijn

Holmes

et al. 2020

Preprint

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“…MFAP2 stimulates epithelial–mesenchymal transition in gastric cancer cells by activating TGFβ signaling pathway that supports survival and metastasis of endometrial cancer cells (Lei et al, 2009). MFAP2 is also related to human endometrial receptivity (Díaz-Gimeno et al, 2011) and has been defined as biochemical pregnancy biomarkers (Fung et al, 2018). Other study found MFAP2 as differentially expressed in severe vs. mild endometriosis (Fung et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…MFAP2 is also related to human endometrial receptivity (Díaz-Gimeno et al, 2011) and has been defined as biochemical pregnancy biomarkers (Fung et al, 2018). Other study found MFAP2 as differentially expressed in severe vs. mild endometriosis (Fung et al, 2018). SMAP2 was reported to be involved in microsatellite instability oncogenesis (Sangar et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data

Akter

Nagel

et al. 2019

Front. Genet.

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Endometriosis is a complex and common gynecological disorder yet a poorly understood disease affecting about 176 million women worldwide and causing significant impact on their quality of life and economic burden. Neither a definitive clinical symptom nor a minimally invasive diagnostic method is available, thus leading to an average of 4 to 11 years of diagnostic latency. Discovery of relevant biological patterns from microarray expression or next generation sequencing (NGS) data has been advanced over the last several decades by applying various machine learning tools. We performed machine learning analysis using 38 RNA-seq and 80 enrichment-based DNA methylation (MBD-seq) datasets. We experimented how well various supervised machine learning methods such as decision tree, partial least squares discriminant analysis (PLSDA), support vector machine, and random forest perform in classifying endometriosis from the control samples trained on both transcriptomics and methylomics data. The assessment was done from two different perspectives for improving classification performances: a) implication of three different normalization techniques and b) implication of differential analysis using the generalized linear model (GLM). Several candidate biomarker genes were identified by multiple machine learning experiments including NOTCH3, SNAPC2, B4GALNT1, SMAP2, DDB2, GTF3C5, and PTOV1 from the transcriptomics data analysis and TRPM6, RASSF2, TNIP2, RP3-522J7.6, FGD3, and MFSD14B from the methylomics data analysis. We concluded that an appropriate machine learning diagnostic pipeline for endometriosis should use TMM normalization for transcriptomics data, and quantile or voom normalization for methylomics data, GLM for feature space reduction and classification performance maximization.

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“…Variation in gene expression in human endometrium is strongly influenced by the stage of the menstrual cycle. The global methylation level of eutopic endometrium was reported to be higher in the proliferative phase as compared to the secretory phase of the menstrual cycle [126].…”

Section: Epigenetic Histone Modificationsmentioning

confidence: 99%

Genetic, Epigenetic, and Steroidogenic Modulation Mechanisms in Endometriosis

Zubrzycka

Zubrzycki

Perdas

et al. 2020

JCM

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Endometriosis is a chronic gynecological disease, affecting up to 10% of reproductive-age women. The exact cause of the disease is unknown; however, it is a heritable condition affected by multiple genetic, epigenetic, and environmental factors. Previous studies reported variations in the epigenetic patterns of numerous genes known to be involved in the aberrant modulation of cell cycle steroidogenesis, abnormal hormonal, immune and inflammatory status in endometriosis, apoptosis, adhesion, angiogenesis, proliferation, immune and inflammatory processes, response to hypoxia, steroidogenic pathway and hormone signaling are involved in the pathogenesis of endometriosis. Accumulating evidence suggest that various epigenetic aberrations may contribute to the pathogenesis of endometriosis. Among them, DNA methyltransferases, histone deacetylators, and non-coding microRNAs demonstrate differential expression within endometriotic lesions and in the endometrium of patients with endometriosis. It has been indicated that the identification of epigenetic differences within the DNA or histone proteins may contribute to the discovery of a useful prognostic biomarker, which could aid in the future earlier detection, timely diagnosis, and initiation of a new approach to the treatment of endometriosis, as well as inform us about the effectiveness of treatment and the stage of the disease. As the etiology of endometriosis is highly complex and still far from being fully elucidated, the presented review focuses on different approaches to identify the genetic and epigenetic links of endometriosis and its pathogenesis.

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Genetic regulation of disease risk and endometrial gene expression highlights potential target genes for endometriosis and polycystic ovarian syndrome

Cited by 47 publications

References 79 publications

Colocalization highlights genes in hypothalamic–pituitary–gonadal axis as potentially mediating polycystic ovary syndrome risk

Colocalization highlights genes in hypothalamic–pituitary–gonadal axis as potentially mediating polycystic ovary syndrome risk

Machine Learning Classifiers for Endometriosis Using Transcriptomics and Methylomics Data

Genetic, Epigenetic, and Steroidogenic Modulation Mechanisms in Endometriosis

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