Preoperative and perioperative intervention reduces the risk of recurrence of endometriosis in mice caused by either incomplete excision or spillage and dissemination

The role of fibrosis in endometriosis: a systematic review

BACKGROUND

Fibrosis is an important pathological feature of endometriotic lesions of all subtypes. Fibrosis is present in and around endometriotic lesions, and a central role in its development is played by myofibroblasts, which are cells derived mainly after epithelial-to-mesenchymal transition (EMT) and fibroblast-to-myofibroblast transdifferentiation (FMT). Transforming growth factor-β (TGF-β) has a key role in this myofibroblastic differentiation. Myofibroblasts deposit extracellular matrix (ECM) and have contracting abilities, leading to a stiff micro-environment. These aspects are hypothesized to be involved in the origin of endometriosis-associated pain. Additionally, similarities between endometriosis-related fibrosis and other fibrotic diseases, such as systemic sclerosis or lung fibrosis, indicate that targeting fibrosis could be a potential therapeutic strategy for non-hormonal therapy for endometriosis.

OBJECTIVE AND RATIONALE

This review aims to summarize the current knowledge and to highlight the knowledge gaps about the role of fibrosis in endometriosis. A comprehensive literature overview about the role of fibrosis in endometriosis can improve the efficiency of fibrosis-oriented research in endometriosis.

SEARCH METHODS

A systematic literature search was performed in three biomedical databases using search terms for 'endometriosis', 'fibrosis', 'myofibroblasts', 'collagen', and 'α-smooth muscle actin'. Original studies were included if they reported about fibrosis and endometriosis. Both preclinical in vitro and animal studies, as well as research concerning human subjects were included.

OUTCOMES

Our search yielded 3441 results, of which 142 studies were included in this review. Most studies scored a high to moderate risk of bias according to the bias assessment tools. The studies were divided in three categories: human observational studies, experimental studies with human-derived material, and animal studies. The observational studies showed details about the histologic appearance of fibrosis in endometriosis and the co-occurrence of nerves and immune cells in lesions. The in vitro studies identified several pro-fibrotic pathways in relation to endometriosis. The animal studies mainly assessed the effect of potential therapeutic strategies to halt or regress fibrosis, for example targeting platelets or mast cells.

WIDER IMPLICATIONS

This review shows the central role of fibrosis and its main cellular driver, the myofibroblast, in endometriosis. Platelets and TGF-β have a pivotal role in pro-fibrotic signaling. The presence of nerves and neuropeptides is closely associated with fibrosis in endometriotic lesions, and is likely a cause of endometriosis-associated pain. The process of fibrotic development after EMT and FMT shares characteristics with other fibrotic diseases, so exploring similarities in endometriosis with known processes in diseases like systemic sclerosis, idiopathic pulmonary fibrosis or liver cirrhosis is relevant and a promising direction to explore new treatment strategies. The close relationship with nerves appears rather unique for endometriosis-related fibrosis and is not observed in other fibrotic diseases.

REGISTRATION NUMBER

N/A.

Construction and evaluation of endometriosis diagnostic prediction model and immune infiltration based on efferocytosis-related genes

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.

Bioinformatics identification and validation of biomarkers and infiltrating immune cells in endometriosis

Background

Endometriosis (EM) is a common gynecological disorder that often leads to irregular menstruation and infertility. The pathogenesis of EM remains unclear and delays in diagnosis are common. Thus, it is urgent to explore potential biomarkers and underlying molecular mechanisms for EM diagnosis and therapies.

Methods

Three EM-related datasets (GSE11691, GSE25628, and GSE86534) were downloaded from the Gene Expression Omnibus (GEO) which were integrated into a combined dataset after removing batch effect. Differentially expressed immune cell-related genes were obtained by CIBERSORT, WGCNA, and the identification of differentially expressed genes. Random forest model (RF), support vector machine model (SVM), and generalized linear model (GLM) were then constructed and the biomarkers for EM were determined. A nomogram evaluating the risk of disease was constructed and the validity was assessed by the calibration curve, DCA curve, and clinical impact curve. Single-gene Gene Set Enrichment Analysis (GSEA)was performed to explore the molecular mechanisms of biomarkers. The ceRNA regulatory network of biomarkers was created by Cytoscape and potential target drugs were obtained in the DGIdb database (Drug-Gene Interaction database).The expression levels of biomarkers from clinical samples was quantified by RT-qPCR.

Results

The ratio of eight immune cells was significantly different between the eutopic and ectopic endometrium samples. A total of eight differentially expressed immune cell-related genes were investigated. The SVM model was a relatively suitable model for the prediction of EM and five genes (CXCL12, PDGFRL, AGTR1, PTGER3, and S1PR1) were selected from the model as biomarkers. The calibration curve, DCA curve, and clinical impact curve indicated that the nomogram based on the five biomarkers had a robust ability to predict disease. Single gene GSEA result suggested that all five biomarkers were involved in labyrinthine layer morphogenesis and transmembrane transport-related biological processes in EM. A ceRNA regulatory network containing 184 nodes and 251 edges was constructed. Seven drugs targeting CXCL12, 49 drugs targeting AGTR1, 16 drugs targeting PTGER3, and 21 drugs targeting S1PR1 were extracted as potential drugs for EM therapy. Finally, the expression of PDGFRL and S1PR1 in clinical samples was validated by RT-qPCR, which was consistent with the result of public database.

Conclusions

In summary, we identified five biomarkers (CXCL12, PDGFRL, AGTR1, PTGER3, and S1PR1) and constructed diagnostic model, furthermore predicted the potential therapeutic drugs for EM. Collectively, these findings provide new insights into EM diagnosis and treatment.