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

Endometrial cancer (EC) is an increasing health concern, with its growth driven by angiogenic switch that occurs early in development. Our study used publicly available datasets to examine the expression of angiogenesis-related genes and proteins EC tissues, compared them adjacent control tissues. We identified nine significant differential selected six additional antiangiogenic from prior research for validation on tissue a cohort 36 patients. Using machine learning, we built prognostic model EC, combining our data The Cancer Genome Atlas (TCGA). results revealed up-regulation IL8 LEP down-regulation eleven other These showed stages lower grades patients without deep myometrial or lymphovascular invasion. Gene co-expressions were stronger particularly those also found more extensive gene involvement postmenopausal women. In conclusion, findings suggest angiogenesis predominantly decreased factor expression, less favourable features. learning effectively stratified based distinguishing between low high-grade cases.