The Polish Society of Gynecological Oncology Guidelines for the Diagnosis and Treatment of Endometrial Carcinoma (2023)

A Machine Learning Approach to Build and Evaluate a Molecular Prognostic Model for Endometrial Cancer Based on Tumour Microenvironment

Endometrial cancer (EC) incidence and the associated tumour burden have increased globally. To build a molecular expression prognostic model based on the tumour microenvironment to guide personalised treatment using a machine learning approach. Two datasets were reviewed, including a training cohort (n = 698) and a testing cohort (n = 151). All patients underwent hysterectomy ± adnexectomy ± lymph nodes dissection between December 2014 and June 2020 at the PLA General Hospital First Medical Center and received necessary and regular follow-up. We developed novel models using R software to predict factors that affect survival, such as progression-free survival and overall survival. Then, the model was optimised by evaluating the prediction efficiency in multiple dimensions. Eight hundred and forty-nine patients with EC were included in the study. Survival-related influences on EC patients were identified by univariate analysis and cox regression equations. In addition, a nomogram was visualised in conjunction with demographic characteristics and the above meaningful clinicopathological variables. Ultimately, through a comprehensive assessment, a random forest model (RF16) was developed for complementing the findings of the molecular classification of EC. The RF16 not only specifically characterises tumour molecules, but also enhances the generalizability of the model by replacing gene sequencing with immunohistochemistry. This study showed that the machine learning model (RF16) is low-cost, efficient, and clinically valuable in guiding treatment for EC patients.

HIF1A, EPAS1, and VEGFA: angiogenesis and hypoxia-related gene expression in endometrium and endometrial epithelial tumors

Endometrial cancer (EC) is the second most frequent gynecological malignancy and the sixth most common women's cancer worldwide. EC incidence rate is increasing rapidly. Apart from the classical, we should consider angiogenesis and hypoxia-related genes as a reason for EC manifestation and progression. We compared the patterns of HIF1A, EPAS1, and VEGFA (genes of interest - GOIs) mRNA expression in 92 cases. HIF1A and VEGFA levels were higher in EC patients than in controls. VEGFA differed significantly between controls and both tumor grades G2 and G3, and we observed a positive correlation for HIF1A and VEGFA with EC grading. VEGFA levels were significantly higher in post-menopausal compared to pre-menopausal patients. All GOIs demonstrated strong correlations in pre-menopausal cases and weak correlations in post-menopausal cases. A positive correlation was observed in pre-menopausal controls for all GOIs and in post-menopausal patients for only EPAS1 and VEGFA. HIF1A and EPAS1 positively correlated with VEGFA in post-menopausal EC cases. Multiple linear regression analyses revealed that menopause, body mass index (BMI), and HIF1A expression are significant stimulating factors for EC occurrence. HIF1A levels were higher in EC patients after BMI and comorbidity number adjustment. The gene-to-gene relation could be seen as either a diagnostic or a therapeutic target in EC. Physicians should inform patients about modifiable risk factors such as BMI. Second, more attention should be paid to diagnosing patients with comorbidities in older age and after menopause. These factors should be considered in designing angiogenesis and hypoxia-related gene-targeting therapies.

Trends in Endometrial Cancer in Poland: Shifts in Clinical Features and Survival Outcomes over 18 Years

Background/Objectives: Endometrial cancer is becoming an even more significant health concern in Poland, with incidence and mortality rates rising each year. Methods: This retrospective study analyzed 1532 patients surgically treated for endometrial cancer at a single center in Poland between 2002 and 2020, examining changes in clinical and histopathological characteristics and their impact on patient outcomes over three time periods: 2003-2008, 2009-2014, and 2015-2020. Results: The study revealed significant shifts in tumor characteristics over time. Early-stage tumors (FIGO IA) increased in prevalence, from 34.1% in 2003-2008 to 49.8% in 2015-2020 (p < 0.001), while advanced-stage cases (FIGO IIIC or higher) decreased from 12.1% to 8.1% (p < 0.001). Similarly, well-differentiated tumors (G1) rose from 46.5% to 62.6% (p < 0.001), while poorly differentiated tumors (G3) decreased slightly from 13.4% to 12.2%. Histologically, the incidence of most typical endometrioid carcinoma peaked at 92.6% in 2009-2014 with 77.4% in 2015-2020 (p < 0.001). The prevalence of serous carcinoma significantly decreased from 16.5% in 2003-2008 to 1.2% in 2009-2014 and 3.2% in 2015-2020. Conclusions:Statistically significant differences in overall survival (OS) across the time periods were found. Three-year OS was 78.0% for patients treated in 2003-2008, compared to 66.2% in 2009-2014 and 69.9% in 2015-2020 (p = 0.024). Similarly, 5-year OS was significantly higher for the 2003-2008 group at 68.8% compared to 50.2% for the 2009-2014 group (p = 0.001). However, progression-free survival (PFS) did not differ significantly at either the 3-year (p = 0.279) or 5-year (p = 0.279) time points.

Organoid development and applications in gynecological cancers: the new stage of tumor treatment

Gynecologic cancers (GCs), including cervical cancer (CC), ovarian cancer (OC), endometrial cancer (EC), as well as vulvar and vaginal cancers, represent major health threats to women, with increasing incidence rates observed globally. Conventional treatments, such as surgery, radiation therapy, and chemotherapy, are often hindered by challenges such as drug resistance and recurrence, contributing to high mortality rates. Organoid technology has emerged as a transformative tool in cancer research, offering in vitro models that closely replicate the tumor cell architecture and heterogeneity of primary cancers. Tumor-derived organoids preserve the histological and molecular characteristics of the original tumors, making them invaluable for studying tumor biology, molecular pathways, and the tumor immune microenvironment. Furthermore, organoids play a crucial role in biomarker discovery, drug screening, and the development of personalized therapeutic strategies. In contrast to traditional cell lines and patient-derived xenograft (PDX) models, gynecologic cancer organoids accurately mirror the genetic mutations and specific gene expression profiles of primary tumors. This review provides an overview of recent advancements in the development of gynecologic cancer organoid models, highlighting their contributions to understanding disease mechanisms, facilitating drug discovery, and advancing precision medicine. It also addresses the potential and challenges of organoid technology, with a focus on its role in advancing personalized treatment approaches for GCs.

RNA 5-Methylcytosine regulators are associated with cell adhesion and predict prognosis of endometrial cancer

Background

RNA methylation is a significant form of post-transcriptional modification that has been implicated in various diseases, including cancers. One prominent type of RNA methylation is 5-Methylcytosine (m5C), which primarily regulates RNA stability, transcription, and translation. However, the role of m5C-related gene regulation in cell adhesion within uterine corpus endometrial carcinoma (UCEC) remains unexplored. Therefore, the objective of this study was to investigate the association between RNA m5C methylation and UCEC and develop a prognostic predictive model to forecast survival outcomes in UCEC patients.

Methods

The RNA datasets were acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The dataset was used to explore the interaction relationships of m5C regulators in UCEC. Unsupervised clustering analysis identified clusters with distinct m5C modification patterns. Different clusters underwent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment level analysis to investigate the effects of pathways related to m5C methylation, which were further validated through in vitro cellular experiments. A prognostic predictive model was developed using the least absolute shrinkage and selection operator (LASSO) and multivariate regression analysis.

Results

Two clusters with distinct m5C modification patterns were identified using unsupervised cluster analysis. Furthermore, the prognosis of cluster 2 was found to be worse. Enrichment analysis showed alterations in cell adhesion-related pathways in both clusters, as well as differences between the clusters. Through this analysis, we identified 25 genes with significant prognostic value. Finally, a prognostic predictive model comprising NSUN2 and YBX1 was constructed.

Conclusions

In conclusion, diverse m5C modification patterns display distinct cell adhesion properties in UCEC, which are correlated with prognosis and offer significant potential as prognostic markers for UCEC assessment. We developed a prognostic predictive model to accurately predict the prognosis of UCEC.