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

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

Endometriosis is a benign, estrogen-dependent, persistent chronic inflammatory heterogeneous condition that features fibrotic adhesions caused by periodic bleeding. The characteristic ectopic lesions are marked by a widely spread dense fibrotic interstitium comprising of fibroblasts, myofibroblasts, collagen fibers, extracellular proteins, inflammatory cells, and active angiogenesis. Fibrosis is now recognized as a critical component of endometriosis 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 noncyclic), dyspareunia, abdominal discomfort, and infertility. The significant lack of knowledge regarding the underlying root causes, etiology, and complex pathogenesis of this debilitating condition, hinders early diagnosis and implement effective therapeutic approaches with minimal side effects presenting substantial hurdles in endometriosis management. Emerging research offer a close relationship between endometriosis and fibrosis, which is believed to be tightly linked to pain, a primary contributor to the deterioration of the patient's quality of life. However, the underlying pathophysiological cellular and molecular signaling pathways behind endometriosis-associated fibrosis are poorly addressed. The available experimental disease models have tremendous challenges in reproducing the human characteristics of the disease limiting the 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 focuses on recent developments in the field and highlight the necessity for novel fibrotic models for early diagnosis, a better understanding the disease's etiology and develop effective anti-fibrotic treatments. By addressing these knowledge gaps, we want to open fresh avenues for a thorough investigation and extended research in the field of endometriosis.

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

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.