Whole-exome sequencing and functional validation reveal a rare missense variant in <i>MMP7</i> that confers ovarian endometriosis risk

Liu, Faying; Zhou, Jiangyan; Fang, Shufen; Liu, Ronghui; Chen, Ge; Luo, Yong; Zhang, Ziyu; Cheng, Yufen; Wang, Liqun; Guo, Jiubai; Zou, Yang

doi:10.1093/hmg/ddac062

“…Collectively, the investigations into genes such as ADAT1, PCSK5, DLGAP5, EPHX1, and c-FOS shed light on the molecular mechanisms underlying this disorder and offer valuable information. Furthermore, other potential vital genes have been identified, for instance, rare variations in MMP7 are significantly enriched in ovarian endometriosis and closely associated with specific clinical features, as first discovered by Liu et al (2022) . Jiang et al introduced LGALS2 and EGR1 as potential new targets for risk prediction and non-invasive diagnosis of endometriosis, providing the potential for personalized medical treatment of EM patients ( Jiang et al, 2023 ).…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis

Zhang,

Yang

et al. 2023

Front. Genet.

2

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Background: Endometriosis (EM) is a common gynecological condition in women of reproductive age, with diverse causes and a not yet fully understood pathogenesis. Traditional diagnostics rely on single diagnostic biomarkers and does not integrate a variety of different biomarkers. This study introduces multiple machine learning techniques, enhancing the accuracy of predictive models. A novel diagnostic approach that combines various biomarkers provides a new clinical perspective for improving the diagnostic efficiency of endometriosis, holding significant potential for clinical application.Methods: In this study, GSE51981 was used as a test set, and 11 machine learning algorithms (Lasso, Stepglm, glmBoost, Support Vector Machine, Ridge, Enet, plsRglm, Random Forest, LDA, XGBoost, and NaiveBayes) were employed to construct 113 predictive models for endometriosis. The optimal model was determined based on the AUC values derived from various algorithms. These genes were then evaluated using nine machine learning algorithms (Random Forest, SVM, Gradient Boosting Machine, LASSO, XGB, NNET, Generalized Linear Model, KNN, and Decision Tree) to assess significance scores and identify diagnostic genes for each algorithm. The diagnostic value of these genes was further validated in external datasets from GSE7305, GSE11691, and GSE120103.Results: Analysis of the GSE51981 dataset revealed 62 DEGs. The Stepglm [Both] and plsRglm algorithms identified 30 genes with the most potential using the AUC evaluation. Subsequently, nine machine learning algorithms were applied to select diagnostic genes, leading to the identification of five key diagnostic genes using the LASSO algorithm. The ADAT1 gene exhibited the best single-gene predictive performance, with an AUC of 0.785. A combination of genes (FOS, EPHX1, DLGAP5, PCSK5, and ADAT1) achieves an AUC of 0.836 in the test dataset. Moreover, these genes consistently exhibited an AUC exceeding 0.78 in all validation datasets, demonstrating superior predictive performance. Furthermore, correlation analysis with immune infiltration strengthened their predictive value by demonstrating the close relationship of the diagnostic genes with immune infiltrating cells.Conclusion: A combination of biomarkers consisting of FOS, EPHX1, DLGAP5, PCSK5, and ADAT1 can serve as a diagnostic tool for endometriosis, enhancing diagnostic efficiency. The association of these genes with immune infiltrating cells reveals their potential role in the pathogenesis of endometriosis, providing new insights for early detection and treatment.

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Unveiling the fibrotic puzzle of endometriosis: An overlooked concern calling for prompt action

Anchan,

Kalthur,

Datta

et al. 2024

F1000Res

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Endometriosis is a benign, estrogen-dependent, persistent chronic inflammatory heterogeneous condition that features adhesions caused by estrogen-dependent periodic bleeding. It is characterised by a widely spread fibrotic interstitium that comprising of fibroblasts, myofibroblasts, collagen fibres, extracellular proteins, inflammatory cells, and active angiogenesis found outside the uterus. Thus, fibrosis is recognized as a critical component because of which current treatments, such as hormonal therapy and surgical excision of lesions are largely ineffective with severe side effects, high recurrence rates, and significant morbidity. The symptoms include dysmenorrhea (cyclic or non-cyclic), dyspareunia, abdominal discomfort, and infertility. The significant lack of knowledge regarding the underlying root cause, etiology, and complex pathogenesis of this debilitating condition, makes it challenging to diagnose early and to implement therapeutic approaches with minimal side effects presenting substantial hurdles in endometriosis management. Research on understanding the pathogenesis of endometriosis is still ongoing to find biomarkers and develop non-hormonal therapeutic approaches. Current clinical research indicates a close relationship between endometriosis and fibrosis, which is thought to be tightly linked to pain, a major factor for the decline in the patient’s quality of life but little is known about the underlying pathophysiological cellular and molecular signaling pathways that lead to endometriosis-related fibrosis. The available experimental disease models have tremendous challenges in reproducing the human characteristics of the disease to assess treatment effectiveness. Future translational research on the topic has been hindered by the lack of an adequate fibrotic model of endometriosis emphasizing the necessity of etiological exploration. This review article’s goal is to examine recent developments in the field and pinpoint knowledge gaps that exist with a focus on the development of novel fibrotic mouse models for the early diagnosis and treatment of endometriosis and how this knowledge aids in the development of novel anti-fibrotic treatments which opens fresh avenues for a thorough investigation and extended research in the field of endometriosis.

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Unveiling the fibrotic puzzle of endometriosis: An overlooked concern calling for prompt action

Anchan,

Kalthur,

Datta

et al. 2024

F1000Res

0

View full text Add to dashboard Cite

Endometriosis is a benign, estrogen-dependent, persistent chronic inflammatory heterogeneous condition that features adhesions caused by estrogen-dependent periodic bleeding. It is characterised by a widely spread fibrotic interstitium that comprising of fibroblasts, myofibroblasts, collagen fibres, extracellular proteins, inflammatory cells, and active angiogenesis found outside the uterus. Thus, fibrosis is recognized as a critical component because of which current treatments, such as hormonal therapy and surgical excision of lesions are largely ineffective with severe side effects, high recurrence rates, and significant morbidity. The symptoms include dysmenorrhea (cyclic or non-cyclic), dyspareunia, abdominal discomfort, and infertility. The significant lack of knowledge regarding the underlying root cause, etiology, and complex pathogenesis of this debilitating condition, makes it challenging to diagnose early and to implement therapeutic approaches with minimal side effects presenting substantial hurdles in endometriosis management. Research on understanding the pathogenesis of endometriosis is still ongoing to find biomarkers and develop non-hormonal therapeutic approaches. Current clinical research indicates a close relationship between endometriosis and fibrosis, which is thought to be tightly linked to pain, a major factor for the decline in the patient’s quality of life but little is known about the underlying pathophysiological cellular and molecular signaling pathways that lead to endometriosis-related fibrosis. The available experimental disease models have tremendous challenges in reproducing the human characteristics of the disease to assess treatment effectiveness. Future translational research on the topic has been hindered by the lack of an adequate fibrotic model of endometriosis emphasizing the necessity of etiological exploration. This review article’s goal is to examine recent developments in the field and pinpoint knowledge gaps that exist with a focus on the development of novel fibrotic mouse models for the early diagnosis and treatment of endometriosis and how this knowledge aids in the development of novel anti-fibrotic treatments which opens fresh avenues for a thorough investigation and extended research in the field of endometriosis.

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Whole-exome sequencing and functional validation reveal a rare missense variant in MMP7 that confers ovarian endometriosis risk

Cited by 4 publications

References 64 publications

Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis

Machine learning-based integrated identification of predictive combined diagnostic biomarkers for endometriosis

Unveiling the fibrotic puzzle of endometriosis: An overlooked concern calling for prompt action

Unveiling the fibrotic puzzle of endometriosis: An overlooked concern calling for prompt action

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