Risk of atypical hyperplasia and endometrial carcinoma after initial diagnosis of non-atypical endometrial hyperplasia: A long-term follow-up study

Obesity-induced extracellular vesicles proteins drive the endometrial cancer pathogenesis: therapeutic potential of HO-3867 and Metformin

Endometrial cancer (EC) is the leading gynecologic malignancy in the United States with obesity implicated in 57% of cases. This research investigates the molecular complexities of extracellular vesicles (EV) secretion as carriers of oncogenic protein and their involvement in obesity-mediated EC. An understanding of these mechanisms is pivotal for unraveling pathways relevant to obesity-associated EC, thereby guiding the development of innovative prevention and treatment strategies. Our exploration revealed a significant increase in EV secretion carrying oncogenic proteins (TMEM205, STAT5, and FAS) in adipose and uterine tissues/serum samples from obese EC patients compared to control (without cancer). We identified alterations in EV-regulating proteins (Rab7, Rab11, and Rab27a) in obesity-mediated EC patients, adipose/uterine tissues, and serum samples. Through a 24-week analysis of the effects of a 45% kcal high-fat diet (HFD) on mice, we observed increased body weight, increased adipose tissue, enlarged uterine horns, and increased inflammation in the HFD group. This correlated with elevated levels of EV secretion and increased expression of oncogenic proteins TMEM205, FAS, and STAT5 and downregulation of the tumor suppressor gene PIAS3 in adipose and uterine tissues. Furthermore, our study confirmed that adipocyte derived EV increased EC cell proliferation, migration and xenograft tumor growth. Additionally, we identified that the small molecule inhibitors (HO-3867) or Metformin inhibited EV secretion in vitro and in vivo, demonstrating significant inhibition of high glucose or adipocyte-mediated EC cell proliferation and a reduction in body weight and adipose tissue accumulation when administered to HFD mice. Moreover, HO-3867 or Metformin treatment inhibited HFD induced hyperplasia (precursor of EC) by altering the expression of EV-regulated proteins and decreasing oncogenic protein expression levels. This study provides critical insights into the mechanisms underpinning obesity-mediated EV secretion with oncogenic protein expression, shedding light on their role in EC pathogenesis. Additionally, it offers pre-clinical evidence supporting the initiation of novel studies for EV-targeted therapies aimed at preventing obesity-mediated EC.

A Review of the Risk Factors Associated with Endometrial Hyperplasia During Perimenopause

Background

Endometrial hyperplasia, characterized by excessive growth leading to endometrial thickening, is commonly observed in the premenopausal period. Its prevalence in postmenopausal women is approximately 15%, peaking between ages 50 and 60. This condition often manifests as abnormal uterine bleeding and can progress to malignancy, with varying risks depending on the type of hyperplasia.

Purpose

This study aims to investigate the factors influencing endometrial thickness during the perimenopausal period and raise awareness among healthcare professionals about the importance of evaluating and caring for individuals with endometrial hyperplasia.

Methods

Studies examining the association between various factors such as diabetes mellitus, hypertension, age, estrogen replacement therapy, anovulatory disorders, smoking, medications, genetic factors, and endocrine-related proteins and the development of endometrial hyperplasia were reviewed. The literature search encompassed relevant databases, including PubMed, Scopus, and Web of Science.

Results

Research findings indicate significant associations between changes in gene expression of several factors and the development of endometrial hyperplasia. Notably, the risk of progression to cancer varies between non-atypical and atypical hyperplasia cases. Factors such as diabetes mellitus, hypertension, age, estrogen replacement therapy, anovulatory disorders, smoking, medications, Lynch syndrome, tamoxifen use, and alterations in gene expression of TNF-α, EGF, IGF-1, IGF-1R, and PTEN have been implicated in the pathogenesis of endometrial hyperplasia.

Conclusion

This study underscores the importance of understanding the factors influencing endometrial thickness during the perimenopausal period. It emphasizes the pivotal role of healthcare professionals in evaluating and caring for individuals with this condition.

Diagnosis and Prediction of Endometrial Carcinoma Using Machine Learning and Artificial Neural Networks Based on Public Databases

Endometrial carcinoma (EC), a common female reproductive system malignant tumor, affects thousands of people with high morbidity and mortality worldwide. This study was aimed at developing a prediction model for the diagnosis of EC in the general population. First, we obtained datasets GSE63678, GSE106191, and GSE115810 from the Gene Expression Omnibus (GEO) database, dataset GSE17025 from the GEO database, and the RNA sequence of EC from The Cancer Genome Atlas (TCGA) database to constitute the training, test, and validation groups, respectively. Subsequently, the 96 most significantly differentially expressed genes (DEGs) were identified and analyzed for function and pathway enrichment in the training group. Next, we acquired the disease-specific genes by random forest and established an artificial neural network for the diagnosis. Receiver operating characteristic (ROC) curves were utilized to identify the signature across the three groups. Finally, immune infiltration was analyzed to reveal tumor-immune microenvironment (TIME) alterations in EC. The top 96 DEGs (77 down-regulated and 19 up-regulated genes) were primarily enriched in the interleukin-17 signaling pathway, protein digestion and absorption, and transcriptional misregulation in cancer. Subsequently, 14 characterizing genes of EC were identified by random forest. In the training, test, and validation groups, the artificial neural network was constructed with high diagnostic accuracies of 0.882, 0.864, and 0.839, respectively, and areas under the ROC curve (AUCs) of 0.928, 0.921, and 0.782, respectively. Finally, resting and activated mast cells were found to have increased in TIME. We constructed an artificial diagnostic model with excellent reliability for EC and uncovered variations in the immunological ecosystem of EC through integrated bioinformatics approaches, which might be potential diagnostic targets for EC.