Establishment of a novel glycolysis-immune-related diagnosis gene signature for endometriosis by machine learning

Endometriosis is not the endometrium: Reviewing the over-representation of eutopic endometrium in endometriosis research

As a heterogenous disease with likely multiple pathogeneses and as-yet-undefined subtypes, progress in endometriosis treatment is currently limited by a lack of appropriate models and cohorts for research. Almost half of all publicly available datasets labelled as 'endometriosis' do not represent true disease as they are based on eutopic endometrium. Eutopic endometrial cells and tissues are frequently being used to represent endometriotic lesions, despite the unequivocal differences at both the tissue and cellular levels. As preclinical endometriosis research increases, it is important that the unique cellular and molecular profiles of endometrium and endometriosis are distinguished. Whilst each of these biospecimens can provide invaluable information to better understand disease aetiology and identify targets for diagnosis and treatment, it is imperative that the appropriate biospecimen and model are used to answer the relevant research question because endometriosis is not the endometrium.

Clues to revising the conventional diagnostic algorithm for endometriosis

Endometriosis is a complex gynecologic disorder characterized primarily by symptoms of pelvic pain, infertility, and altered quality of life. National and international guidelines highlight the diagnostic difficulties and lack of conclusive diagnostic tools for endometriosis. Furthermore, guidelines are becoming questionable at an increasingly rapid rate as new diagnostic techniques emerge. This work aims to provide a knowledge synthesis of the relevance of various diagnostic tools and to assess areas of improvement of conventional algorithms. MEDLINE and Cochrane Library databases were searched from January 2021 to December 2023 using relevant key words. Articles evaluating the diagnostic relevance and performance of various tools were included and independently reviewed by the authors for eligibility. Included studies were assessed using the GRADE and QUADAS-2 tools. Of the 4204 retrieved articles, 26 were included. While anamnesis and clinical examination do contribute to diagnostic accuracy, their level of evidence and impact on the diagnostic process remains limited. Although imaging techniques are recommended to investigate endometriosis, ultrasonography remains highly operator dependent. Magnetic resonance imaging appears to exhibit higher sensitivities than ultrasound. However, concerns persist with regards to the terminology, anatomical definition of lesions, and accuracies of both ultrasound and magnetic resonance imaging. Recently, several biological markers have been studied and cumulative evidence supports the contribution of noncoding RNAs to the diagnosis of endometriosis. Marginal improvements have been suggested for anamnesis, clinical examination, and imaging examinations. Conversely, some biomarkers, including the saliva microRNA signature for endometriosis, have emerged as diagnostic tools which inspire reflection on the revision of conventional diagnostic algorithms.

Biotechnological progresses in modelling the human endometrium: the evolution of current in vitro techniques and emerging trends

The endometrium plays a fundamental role in the reproductive system yet many etiologies of infertility-related endometrial diseases such as endometriosis, adenomyosis, Asherman's syndrome or endometrial cancer remain unknown. There are currently no treatments that minimize the effects of this devastating disorder. Appropriate model systems that closely mimic the architecture and function of the endometrium in healthy and pathological states are needed to understand the underlying molecular pathways and develop novel or more effective treatments. This review summarizes the key milestones of in vitro culture models of the human endometrium throughout history, as well as the applications of advanced bioengineering techniques in the modelling of both healthy and pathological endometrium. Opportunities for future approaches are also discussed.

Correlation of Glycolysis-immune-related Genes in the Follicular Microenvironment of Endometriosis Patients with ART Outcomes

Endometriosis (EMT) -related infertility has been a challenge for clinical research. Many studies have confirmed that abnormal alterations in the immune microenvironment and glycolysis are instrumental in causing EMT-related infertility. Recently, our research team identified several key glycolysis-immune-related genes in the endometrial cells of EMT patients. This study aimed to further investigate the expression patterns of pyruvate dehydrogenase kinase 3 (PDK3), glypican-3 (GPC3), and alcohol dehydrogenase 6 (ADH6), which are related to glycolysis and immunity, in the follicular microenvironment of infertile patients with EMT using enzyme-linked immunosorbent assay (ELISA) and quantitative real-time polymerase chain reaction (qRT-PCR) assays. According to the results, compared to the patients with tubal factor infertility, the concentrations of PDK3 and GPC3 were considerably increased in the follicular environment of EMT patients, while ADH6 expression was significantly reduced. The number of oocytes retrieved, the transferable embryo rate, and the cumulative clinical pregnancy rate of EMT patients were significantly reduced, and there was a correlation with the level of PDK3, GPC3, and ADH6 in Follicular Fluid (FF). The area under the receiver operating characteristic (ROC) curve for predicting clinical pregnancy in infertile patients with EMT for PDK3, GPC3, ADH6, and their combination was 0.732, 0.705, 0.855, and 0.879, respectively (P < 0.05). In conclusion, our research indicates that glycolysis-immune-related genes may contribute to infertility in EMT patients through immune infiltration, and disruption of mitochondrial and oocyte functions. The combined detection of PDK3, GPC3, and ADH6 in FF helps to predict clinical pregnancy outcomes in infertile patients with EMT.

Identifying a panel of nine genes as novel specific model in endometriosis noninvasive diagnosis

OBJECTIVE

To study biomarkers to develop a novel diagnosis model for endometriosis and validate it using clinical samples.

DESIGN

We used publicly available data sets and weighted gene coexpression network analysis to identify differentially expressed genes. Ten machine learning algorithms were used to develop an integrative model for predicting endometriosis. The accuracy and robustness of the model were validated using data sets and clinical samples.

SETTING

Department of Obstetrics and Gynecology, Tangdu Hospital, Air Force Medical University, Xi'an, Shaanxi, China.

PATIENT(S)

The study included clinical patients between the ages of 20 and 40 years who required laparoscopic surgery and who had not undergone hormone therapy within the previous 3 months. All the healthy individuals had given birth to a child at least once in their lives. Patients with inflammatory conditions, malignant diseases, immune diseases, myoma, or adenomyosis were excluded. Paraffin blocks of the samples were collected (case, n = 5; control, n = 5). Blood samples of 58 individuals were collected (case, n = 28; control, n = 30).

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

The areas under the receiver operator characteristic curve of our diagnostic model were measured for data sets and clinical samples. Multiplex immunohistochemical staining and real-time quantitative polymerase chain reaction assays were used for the validation of the model from tissue slides and peripheral blood samples.

RESULT(S)

A nine-gene panel endometriosis messenger RNA score (EMScore), was constructed to distinguish the patients with endometriosis from healthy individuals using algorithms. The EMScore accurately predicted endometriosis, and the areas under the receiver operator characteristic curve of our diagnostic model were 0.920, and 0.942 for tissue and blood samples, respectively. Moreover, the EMScore outperformed other acknowledged signatures for predicting endometriosis across seven clinical cohorts. Overall, the EMScore constitutes a sensitive and specific noninvasive diagnostic method for endometriosis.

CONCLUSION(S)

We developed the EMScore, a novel model that can aid in the diagnosis of endometriosis using peripheral blood samples. This study will contribute to the development of improved clinical noninvasive and sensitive diagnostic tools for endometriosis. These nine genes might be potential target molecules for treating endometriosis.