Serum miRNA-based diagnostic models for endometriosis: from discovery to validation.

Identification of Cells with Colony-Forming Activity, Self-Renewal Capacity, and Multipotency in Ovarian Endometriosis

Background: Endometriosis (EM) is a long-lasting inflammatory disease that is difficult to treat and prevent. Existing research indicates the significance of immune infiltration in the progression of EM. Efferocytosis has an important immunomodulatory function. However, research on the identification and clinical significance of efferocytosis-related genes (EFRGs) in EM is sparse. Methods: The EFRDEGs (differentially expressed efferocytosis-related genes) linked to datasets associated with endometriosis were thoroughly examined utilizing the Gene Expression Omnibus (GEO) and GeneCards databases. The construction of the protein-protein interaction (PPI) and transcription factor (TF) regulatory network of EFRDEGs ensued. Subsequently, machine learning techniques including Univariate logistic regression, LASSO, and SVM classification were applied to filter and pinpoint diagnostic biomarkers. To establish and assess the diagnostic model, ROC analysis, multivariate regression analysis, nomogram, and calibration curve were employed. The CIBERSORT algorithm and single-cell RNA sequencing (scRNA-seq) were employed to explore immune cell infiltration, while the Comparative Toxicogenomics Database (CTD) was utilized for the identification of potential therapeutic drugs for endometriosis. Finally, immunohistochemistry (IHC) and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were utilized to quantify the expression levels of biomarkers in clinical samples of endometriosis. Results: Our findings revealed 13 EFRDEGs associated with EM, and the LASSO and SVM regression model identified six hub genes (ARG2, GAS6, C3, PROS1, CLU, and FGL2). Among these, ARG2, GAS6, and C3 were confirmed as diagnostic biomarkers through multivariate logistic regression analysis. The ROC curve analysis of GSE37837 (AUC = 0.627) and GSE6374 (AUC = 0.635), along with calibration and DCA curve assessments, demonstrated that the nomogram built on these three biomarkers exhibited a commendable predictive capacity for the disease. Notably, the ratio of nine immune cell types exhibited significant differences between eutopic and ectopic endometrial samples, with scRNA-seq highlighting M0 Macrophages, Fibroblasts, and CD8 Tex cells as the cell populations undergoing the most substantial changes in the three biomarkers. Additionally, our study predicted seven potential medications for EM. Finally, the expression levels of the three biomarkers in clinical samples were validated through RT-qPCR and IHC, consistently aligning with the results obtained from the public database. Conclusion: we identified three biomarkers and constructed a diagnostic model for EM in this study, these findings provide valuable insights for subsequent mechanistic research and clinical applications in the field of endometriosis.

Background. Endometriosis is defined as a chronic, inflammatory, estrogen-dependent gynaecologic disease. It affects approximately 5–10% of reproductive-age women worldwide. Ovarian endometriosis is the most frequent form of this condition. Endometriotic cysts are found in about 17–44% of women diagnosed with endometriosis. It is well known, that ovarian endometriomas can cause infertility and chronic pelvic pain. Enlarging cysts can also cause ovarian torsion. In addition, ovarian endometriosis slightly increases the risk for ovarian cancer. The rupture of endometriotic ovarian cysts is an exceptional complication. According to the literature, the prevalence is less than 3% among women with endometriosis. The rupture of an ovarian endometrioma can cause acute peritonitis, which can lead to sepsis, septic shock and can be lethal. The occurrence of abscesses within an ovarian endometrioma is an extremely rare complication. Generally, the origin of infected endometriotic ovarian cysts is related to the previous invasive procedures involving pelvic organs or the use of intrauterine devices. Also, ovarian abscesses can be caused by the hematogenous or lymphatic spread of bacteria. Although, the literature points out that infection of endometriotic ovarian cysts can develop spontaneously. In these rare cases, reservoir and route of infection remains an enigma.Case report.A 49-year-old female was brought to the emergency room with severe generalized lower abdominal pain (6/10) and three days lasting fever. Abdominal examination revealed diffuse abdominal pain with anterior abdominal wall muscle tension. Painful solid masses were felt on both sides of the uterus during the pelvic examination. Cystic masses were detected in both ovaries during transvaginal sonography. Computer tomography (CT) of the abdomen and pelvis revealed abnormal changes in both ovaries. A small amount of free fluid was found in the pelvic cavity along with thickened pelvic peritoneum. Suspecting acute peritonitis and bilateral tubo-ovarian abscesses, surgical treatment was performed. Lower midline laparotomy with bilateral adnexectomy and abdominal lavage with 4000 ml normal saline were done. The outcome of peritonitis was evaluated using the Mannheim peritonitis index (MPI=17 – low risk of morbidity and mortality). The histopathological examination revealed the diagnosis of bilateral endometriotic cysts complicated with acute inflammation, with associated acute inflammation of both fallopian tubes. Microbiological cultures from the purulent fluid were negative.Conclusions.Although the occurrence of abscesses within an ovarian endometrioma is an extremely rare finding in clinical practice, it has to be considered by gynaecologists because it might result in a surgical emergency that can be life-threatening. Being aware of the risk factors of abscesses within an endometrioma can lead to an early diagnosis of this rare condition and help to avoid serious complications.

Interleukin-10 attenuates TNF-α–induced interleukin-6 production in endometriotic stromal cells

Could peritoneal fluid (PF) from patients with endometriosis alter the microRNA (miRNA) expression profile in endometrial and endometriotic cells from patients? PF from patients with endometriosis modifies the miRNA expression profile in endometrial cells from patients. Angiogenesis is a pivotal system in the development of endometriosis, and dysregulated miRNA expression in this disease has been reported. However, to our knowledge, the effect of PF from patients on the miRNA expression profile of patient endometrial cells has not been reported. Moreover, an effect of three miRNAs (miR-16-5p, miR-29c-3p and miR-424-5p) on the regulation of vascular endothelial growth factor (VEGF)-A mRNA translation in endometrial cells from patients with endometriosis has not been demonstrated. Primary cultures of stromal cells from endometrium from 8 control women (control cells) and 11 patients with endometriosis (eutopic cells) and ovarian endometriomas (ectopic cells) were treated with PF from control women (CPF) and patients (EPF) or not treated (0PF) in order to evaluate the effect of PF on miRNA expression in these cells. MiRNA expression arrays (Affymetrix platform) were prepared from cells (control, eutopic, ectopic) treated with CPF, EPF or 0PF. Results from arrays were validated by quantitative reverse transcription-polymerase chain reaction in cultures from 8 control endometrium, 11 eutopic endometrium and 11 ovarian endometriomas. Functional experiments were performed in primary cell cultures using mimics for miRNAs miR-16-5p, miR-29c-3p and miR-424-5p to assess their effect as VEGF-A expression regulators. To confirm a repressive action of miR-29c-3p through forming miRNA:VEGFA duplexes, we performed luciferase expression assays. EPF modified the miRNA expression profile in eutopic cells. A total of 267 miRNAs were modified in response to EPF compared with 0PF in eutopic cells. Nine miRNAs (miR-16-5p, miR-21-5p, miR-29c-3p, miR-106b-5p, miR-130a-5p, miR-149-5p, miR-185-5p, miR-195-5p, miR-424-5p) that were differently expressed in response to EPF, and which were potential targets involved in angiogenesis, proteolysis or endometriosis, were validated in further experiments (control = 8, eutopic = 11, ectopic = 11). Except for miR-149-5p, all validated miRNAs showed significantly lower levels (miR-16-5p, miR-106b-5p, miR-130a-5p; miR-195-5p and miR-424-5p, P < 0.05; miR-21-5p, miR-29c-3p and miR-185-5p, P < 0.01) after EPF treatment in primary cell cultures from eutopic endometrium from patients in comparison with 0PF. Transfection of stromal cells with mimics of miRNAs miR-16-5p, miR-29c-3p and miR-424-5p showed a significant down-regulation of VEGF-A protein expression. However, VEGFA mRNA expression after mimic transfection was not significantly modified, indicating the miRNAs inhibited VEGF-A mRNA translation rather than degrading VEGFA mRNA. Luciferase experiments also corroborated VEGF-A as a target gene of miR-29c-3p. The study was performed in an in vitro model of endometriosis using stromal cells. This model is just a representation to try to elucidate the molecular mechanisms involved in the development of endometriosis. Further studies to identify the pathways involved in this miRNA expression modification in response to PF from patients are needed. This is the first study describing a modified miRNA expression profile in eutopic cells from patients in response to PF from patients. These promising results improve the body of knowledge on endometriosis pathogenesis and could open up new therapeutic strategies for the treatment of endometriosis through the use of miRNAs. This work was supported by research grants by ISCIII and FEDER (PI11/00091, PI11/00566, PI14/01309, PI14/00253 and FI12/00012), RIC (RD12/0042/0029 and RD12/0042/0050), IIS La Fe 2011-211, Prometeo 2011/027 and Contrato Sara Borrell CD13/0005. There are no conflicts of interest to declare.

Do endometriotic ovarian cysts influence the rate of spontaneous ovulation? Endometriotic cysts, no matter what their volume, do not influence the rate of spontaneous ovulation in the affected ovary. Endometriotic ovarian cysts may negatively affect spontaneous ovulation in the affected ovary. This was a prospective observational study performed between September 2009 and June 2013. This study included women of reproductive age with regular menstrual cycles and unilateral ovarian endometriomas (diameter ≥20 mm) desiring to conceive. Exclusion criteria were: hormonal therapies in the 3 months prior to study entry and previous adnexal surgery. Patients underwent serial transvaginal ultrasound to assess the side of ovulation (for up to six cycles). Ovulation was monitored in 1199 cycles in 244 women (age, mean ± SD, 34.3 ± 4.9 years). 55.3% of the patients had left endometriomas and 44.7% had right endometriomas (P = 0.024). The mean (±SD) diameter of the endometriomas was 5.3 cm (±1.7 cm). Ultrasonographically documented ovulation occurred in 596 cycles in the healthy ovary (49.7%; 95% CI, 46.8-52.6%) and in 603 cycles in the affected ovary (50.3%; 95% CI, 47.1-53.2%; P = 0.919). This observation was confirmed in patients with diameter of the cyst ≥4 cm (n = 166) and in those with diameter of the cyst ≥6 cm (n = 45). One hundred and five patients spontaneously conceived (43.0%; 95% CI, 36.7-49.5%). The high pregnancy rate reported in this study was observed in a selected population of women with endometriomas and cannot be extrapolated to all patients with endometriosis. Since ovarian endometriomas do not impair spontaneous ovulation, the impact on fertility of surgical excision of ovarian endometriomas should be further investigated.

Cyclooxygenase-2 overexpression in ovarian endometriomas is associated with higher risk of recurrence

Gonadotropin-releasing hormone agonist treatment reduced serum interleukin-6 concentrations in patients with ovarian endometriomas

Endometriosis caused by retrograde menstruation: now demonstrated by DNA evidence

Endometriosis is a chronic gynecological disorder characterized by the ectopic growth of endometrial tissue outside the uterus, leading to debilitating pain and infertility in affected women. Despite its prevalence and clinical significance, the molecular mechanisms underlying the progression of endometriosis remain poorly understood. This study employs bioinformatics tools and molecular docking simulations to unravel the intricate genetic and molecular networks associated with endometriosis progression. The primary objectives of this research are to identify differentially expressed genes (DEGs) linked to endometriosis, elucidate associated biological pathways using the Database for Annotation, Visualization, and Integrated Discovery (DAVID), construct a Protein-Protein Interaction (PPI) network to identify hub genes, and perform molecular docking simulations to explore potential ligand-protein interactions associated with endometriosis. Microarray data from Homo sapiens, specifically Accession: GDS3092 Series = GSE5108 (Platform: GPL2895), were retrieved from the NCBI Gene Expression Omnibus (GEO). The data underwent rigorous preprocessing and DEG analysis using NCBI GEO2. Database for Annotation, Visualization, and Integrated Discovery analysis was employed for functional annotation, and a PPI network was constructed using the STITCH database and Cytoscape 3.8.2. Molecular docking simulations against target proteins associated with endometriosis were conducted using MVD 7.0. A total of 1 911 unique elements were identified as DEGs associated with endometriosis from the microarray data. Database for Annotation, Visualization, and Integrated Discovery analysis revealed pathways and biological characteristics positively and negatively correlated with endometriosis. Hub genes, including BCL2, CCNA2, CDK7, EGF, GAS6, MAP3K7, and TAB2, were identified through PPI network analysis. Molecular docking simulations highlighted potential ligands, such as Quercetin-3-o-galactopyranoside and Kushenol E, exhibiting favorable interactions with target proteins associated with endometriosis. This study provides insights into the molecular signatures, pathways, and hub genes associated with endometriosis. Utilizing DAVID in this study clarifies biological pathways associated with endometriosis, revealing insights into intricate genetic networks. Molecular docking simulations identified ligands for further exploration in therapeutic interventions. The consistent efficacy of these ligands across diverse targets suggests broad-spectrum effectiveness, encouraging further exploration for potential therapeutic interventions. The study contributes to a deeper understanding of endometriosis pathogenesis, paving the way for targeted therapies and precision medicine approaches to improve patient outcomes. These findings advance our understanding of the molecular mechanisms in endometriosis (EMS), offering promising avenues for future research and therapeutic development in addressing this complex condition.

ERβ regulates ERα expression and response to estradiol via specific promoters in endometrium and endometriosis

To investigate whether asymmetry exists in the left- and right-handed distribution of ovarian cystic lesions in a large series of women with endometriosis. Retrospective evaluation of a case series. Tertiary care and referral academic centre for the study and treatment of endometriosis. A total of 1054 consecutive women undergoing first-line surgical treatment for endometriosis in an eight-year period. Data were collected on indication for the intervention, age at surgery, parity and disease stage as well as side and size of ovarian endometriomas. Frequency of left- and right-sided ovarian endometriomas. Histologically confirmed endometriotic ovarian cysts were present in 561 women, which were on the left side in 255 instances, on the right in 148, and bilateral in 158. In the patients with unilateral endometriomas, the observed proportion of left cysts (255/403, 63%; 95% confidence interval, 58% to 68%) was significantly different from the expected proportion of 50%, (chi2(1), 28.41, P<0.001). Including also the bilateral endometriotic cysts gave a total of 413/719 (57%) left-sided and 306/719 right-sided endometriomas. The magnitude of these proportions did not vary appreciably during the eight years considered. The difference in proportion of left- and right-sided endometriotic cysts was virtually similar in subgroups of women with different indications for surgery. Cyst side was not related to age, parity or cyst diameter. The finding of a lateral asymmetry in the occurrence of ovarian endometriotic cysts is compatible with the anatomical differences of the left and right hemipelvis and supports the menstrual reflux theory.

Endometriosis (EM), an estrogen-dependent inflammatory disease with unknown etiology, affects thousands of childbearing-age couples, and its early diagnosis is still very difficult. With the rapid development of sequencing technology in recent years, the accumulation of many sequencing data makes it possible to screen important diagnostic biomarkers from some EM-related genes. In this study, we utilized public datasets in the Gene Expression Omnibus (GEO) and Array-Express database and identified seven important differentially expressed genes (DEGs) (COMT, NAA16, CCDC22, EIF3E, AHI1, DMXL2, and CISD3) through the random forest classifier. Among these DEGs, AHI1, DMXL2, and CISD3 have never been reported to be associated with the pathogenesis of EMs. Our study indicated that these three genes might participate in the pathogenesis of EMs through oxidative stress, epithelial–mesenchymal transition (EMT) with the activation of the Notch signaling pathway, and mitochondrial homeostasis, respectively. Then, we put these seven DEGs into an artificial neural network to construct a novel diagnostic model for EMs and verified its diagnostic efficacy in two public datasets. Furthermore, these seven DEGs were included in 15 hub genes identified from the constructed protein–protein interaction (PPI) network, which confirmed the reliability of the diagnostic model. We hope the diagnostic model can provide novel sights into the understanding of the pathogenesis of EMs and contribute to the clinical diagnosis and treatment of EMs.

Dienogest enhances autophagy induction in endometriotic cells by impairing activation of AKT, ERK1/2, and mTOR

Endometriosis (EMs) is a major gynecological condition in women. Due to the absence of definitive symptoms, its early detection is very challenging; thus, it is crucial to find biomarkers to ease its diagnosis and therapy. Here, we aimed to identify potential diagnostic and therapeutic targets for EMs by constructing a regulatory network and using machine learning approaches. Three Gene Expression Omnibus (GEO) datasets were merged, and differentially expressed genes (DEGS) were identified after preprocessing steps. Using the DEGs, a transcription factor (TF)-mRNA-miRNA regulatory network was constructed, and hub genes were detected based on four different algorithms in CytoHubba. The hub genes were used to build a GaussianNB diagnostic model and also in docking analysis that were performed using Discovery Studio and AutoDock Vina software. A total of 119 DEGs were identified between EMs and non-EMs samples. A regulatory network consisting of 52 mRNAs, 249 miRNAs, and 37 TFs was then constructed. The diagnostic model was introduced using the hub genes selected from the network (GATA6, HMOX1, HS3ST1, NFASC, and PTGIS) that its area under the curve (AUC) was 0.98 and 0.92 in the training and validation cohorts, respectively. Based on docking analysis, two chemical compounds, rofecoxib and retinoic acid, had potential therapeutic effects on EMs. In conclusion, this study identified potential diagnostic and therapeutic targets for EMs which demand more experimental confirmations.