ABSTRACT
Endometrial cancer (EC) is an increasing health concern, with its growth driven by an angiogenic switch that occurs early in cancer development. Our study used publicly available datasets to examine the expression of angiogenesis-related genes and proteins in EC tissues, and compared them with adjacent control tissues. We identified nine genes with significant differential expression and selected six additional antiangiogenic genes from prior research for validation on EC tissue in a cohort of 36 EC patients. Using machine learning, we built a prognostic model for EC, combining our data with The Cancer Genome Atlas (TCGA). Our results revealed a significant up-regulation of IL8 and LEP and down-regulation of eleven other genes in EC tissues. These genes showed differential expression in the early stages and lower grades of EC, and in patients without deep myometrial or lymphovascular invasion. Gene co-expressions were stronger in EC tissues, particularly those with lymphovascular invasion. We also found more extensive angiogenesis-related gene involvement in postmenopausal women. In conclusion, our findings suggest that angiogenesis in EC is predominantly driven by decreased antiangiogenic factor expression, particularly in EC with less favourable prognostic features. Our machine learning model effectively stratified EC based on gene expression, distinguishing between low and high-grade cases.
ABSTRACT
Background: The diversity of endometrial cancer (EC) dictates the need for precise early diagnosis and pre-operative stratification to select treatment options appropriately. Non-invasive biomarkers invaluably assist clinicians in managing patients in daily clinical practice. Currently, there are no validated diagnostic or prognostic biomarkers for EC that could accurately predict the presence and extent of the disease. Methods: Our study analyzed 202 patients, of whom 91 were diagnosed with EC and 111 were control patients with the benign gynecological disease. Using Luminex xMAP™ multiplexing technology, we measured the pre-operative plasma concentrations of six previously selected angiogenic factors - leptin, IL-8, sTie-2, follistatin, neuropilin-1, and G-CSF. Besides basic statistical methods, we used a machine-learning algorithm to create a robust diagnostic model based on the plasma concentration of tested angiogenic factors. Results: The plasma levels of leptin were significantly higher in EC patients than in control patients. Leptin was higher in type 1 EC patients versus control patients, and IL-8 was higher in type 2 EC versus control patients, particularly in poorly differentiated endometrioid EC grade 3. IL-8 plasma levels were significantly higher in EC patients with lymphovascular or myometrial invasion. Among univariate models, the model based on leptin reached the best results on both training and test datasets. A combination of age, IL-8, leptin and G-CSF was determined as the most important feature for the multivariate model, with ROC AUC 0.94 on training and 0.81 on the test dataset. The model utilizing a combination of all six AFs, BMI and age reached a ROC AUC of 0.89 on both the training and test dataset, strongly indicating the capability for predicting the risk of EC even on unseen data. Conclusion: According to our results, measuring plasma concentrations of angiogenic factors could, provided they are confirmed in a multicentre validation study, represent an important supplementary diagnostic tool for early detection and prognostic characterization of EC, which could guide the decision-making regarding the extent of treatment.
ABSTRACT
Preoperative determination of the extent of endometrial cancer (EC) would avoid the complications associated with radical surgery. Screening of patients' plasma biomarkers might enable a more precise diagnosis of EC and a tailored treatment approach. This prospective case-control monocentric pilot study included 76 postmenopausal women (38 endometrioid EC patients and 38 control patients with benign gynecological conditions), and 37 angiogenic factors (AFs) were investigated as potential biomarkers for EC. AF concentrations in preoperative plasma samples were measured using Luminex xMAP™ multiplexing technology. The plasma levels of sTie-2 and G-CSF were significantly lower in EC compared to control patients, whereas the plasma levels of leptin were significantly higher in EC patients. Neuropilin-1 plasma levels were significantly higher in patients with type 2 EC (grade 3) compared to patients with lower grade cancer or controls. Follistatin levels were significantly higher in patients with lymphovascular invasion, and IL-8 plasma levels were significantly higher in patients with metastases. If validated, the plasma concentrations of the indicated AFs could represent an important additional diagnostic tool for the early detection and characterization of EC. This could guide the decision-making on the extent of surgery. Further studies with larger patient numbers are currently ongoing.
ABSTRACT
Endometrial cancer (EC) is the most frequent gynecological malignancy in developed countries and requires a relatively invasive diagnostic evaluation and operative therapy as the primary therapeutic approach. Angiogenesis is one of the main processes needed for cancer growth and spread. The production of angiogenic factors (AFs) appears early in the process of carcinogenesis. The detection of AFs in plasma and tissue and a better understanding of the angiogenic properties of EC may contribute not only to earlier but also more specific diagnosis and consequently tailored and individual therapeutic approaches. AFs and their receptors also have high potential as binding sites for targeted cancer therapy. In this review, we discuss angiogenesis in EC and the characteristics of the AFs that most contribute to angiogenesis in EC. We also highlight therapeutic strategies that target angiogenesis as potential EC therapy.
