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Using publicly available datasets, the study compares angiogenesis-related gene and protein expression in EC tissue versus adjacent controls, then validates results in a cohort of 36 EC patients. IL8 and LEP are significantly up-regulated, while eleven other genes are down-regulated, with stronger gene co-expression in EC tissue, especially with lymphovascular invasion. The work further identifies differential involvement by stage, grade, and postmenopausal status, and develops a machine learning prognostic model based on gene expression, distinguishing low- versus high-grade EC.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/decreased-gene-expression-of-antiangiogenic-factors-in-endometrial-cancer-qpcr-analysis-and-machine-learning-modelling/381377/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/decreased-gene-expression-of-antiangiogenic-factors-in-endometrial-cancer-qpcr-analysis-and-machine-learning-modelling/381377.png","ImageObject",300,407,{"name":92,"@type":93},"Maya Linwood","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-26","2026-09-24",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What datasets and validation cohort were used in the study?","Question",{"text":112,"@type":113},"The study first uses publicly available datasets to compare angiogenesis-related gene and protein expression in EC tissue and adjacent controls, then validates findings in a cohort of 36 EC patients.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which genes showed the strongest changes in endometrial cancer tissue?",{"text":117,"@type":113},"The results show significant up-regulation of IL8 and LEP, alongside down-regulation of eleven other genes in EC tissue.",{"name":119,"@type":110,"acceptedAnswer":120},"How was machine learning applied, and what did the model achieve?",{"text":121,"@type":113},"A machine learning prognostic model for EC was built using gene expression data, combining the study’s data with TCGA, and it effectively stratified EC by distinguishing low- versus high-grade cases.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},381377,1790385054,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":26},962084928432,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","cancers   \nArticle  \nDecreased Gene Expression of Antiangiogenic Factors in Endometrial Cancer: qPCR Analysis and Machine Learning Modelling  \nLuka Roškar 1,2, Marko Kokol 3,4, Renata Pavliˇc 5, Irena Roškar 5, Špela Smrkolj 1,6 and Tea Lanišnik Rižner 5, *  \nCitation: Roškar, L.; Kokol, M.; Pavliˇc, R.; Roškar, I.; Smrkolj, Š.; Rižner, T.L. Decreased Gene Expression of Antiangiogenic Factors in Endometrial Cancer: qPCR Analysis and Machine Learning Modelling. Cancers 2023, 15, 3661 . [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)cancers15143661  \nAcademic Editor: Vito Andrea Capozzi  \nReceived: 15 June 2023  \nRevised: 13 July 2023  \nAccepted: 14 July 2023  \nPublished: 18 July 2023  \nCopyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Gynaecology and Obstetrics, Faculty of Medicine, University of Ljubljana,  \n1000 Ljubljana, Slovenia; [spela.smrkolj@mf.uni-lj.si](spela.smrkolj@mf.uni-lj.si) (Š .S.)  \n2 Division of Gynaecology and Obstetrics, General Hospital Murska Sobota, 9000 Murska Sobota, Slovenia  \n3 Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia  \n4 Semantika Research, Semantika d.o.o., 2000 Maribor, Slovenia  \n5 Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana,  \n1000 Ljubljana, Slovenia  \n6 Division of Gynaecology and Obstetrics, University Medical Centre, 1000 Ljubljana, Slovenia  \n* Correspondence: [tea.lanisnik-rizner@mf.uni-lj.si](tea.lanisnik-rizner@mf.uni-lj.si)  \nSimple 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.  \nAbstract: Endometrial cancer (EC) is an increasing health concern, with its growth driven by anangiogenic 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 ","cbCaioyduvN8Ajkm","https://ap.wps.com/l/cbCaioyduvN8Ajkm","pdf",5124516,24,"English","# Simple Summary\n# Abstract\n# Keywords\n# 1. Introduction","[{\"question\":\"What datasets and validation cohort were used in the study?\",\"answer\":\"The study first uses publicly available datasets to compare angiogenesis-related gene and protein expression in EC tissue and adjacent controls, then validates findings in a cohort of 36 EC patients.\"},{\"question\":\"Which genes showed the strongest changes in endometrial cancer tissue?\",\"answer\":\"The results show significant up-regulation of IL8 and LEP, alongside down-regulation of eleven other genes in EC tissue.\"},{\"question\":\"How was machine learning applied, and what did the model achieve?\",\"answer\":\"A machine learning prognostic model for EC was built using gene expression data, combining the study’s data with TCGA, and it effectively stratified EC by distinguishing low- versus high-grade cases.\"}]","Decreased Gene Expression of Antiangiogenic Factors in Endometrial Cancer - qPCR Analysis and Machine Learning Modelling | PDF",1790245066]