Decreased Gene Expression of Antiangiogenic Factors in Endometrial Cancer: qPCR Analysis and Machine Learning Modelling

SIMPLE SUMMARY: Endometrial cancer (EC) is a prevalent gynaecological cancer, the growth and spread of which are facilitated by angiogenesis. Our study used publicly available datasets to compare the expression of angiogenesis-related genes and proteins in EC tissue and adjacent controls. We validated these findings in a cohort of 36 EC patients and built an EC-grade prediction model using machine learning. The results showed a significant up-regulation of IL8 and LEP and down-regulation of 11 other genes in EC tissue. These genes were differentially expressed in early-stage and lower-grade EC but not in more advanced forms or in patients with deep myometrial or lymphovascular invasion. Gene co-expressions were stronger in EC tissue, especially when the lymphovascular invasion was present. More extensive angiogenesis-related gene involvement was seen in postmenopausal women. Our findings suggest that angiogenesis in EC is primarily driven by reduced antiangiogenic factor expression, with altered regulation in the tumour-adjacent tissue of EC patients with less favourable prognoses. 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.