{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T16:45:00Z","timestamp":1791477900488,"version":"4.3.4"},"reference-count":32,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:00:00Z","timestamp":1740096000000},"content-version":"vor","delay-in-days":20,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Cellular Molecular Medi"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>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 (<jats:italic>n<\/jats:italic>\u2009=\u2009698) and a testing cohort (<jats:italic>n<\/jats:italic>\u2009=\u2009151). All patients underwent hysterectomy\u2009\u00b1\u2009adnexectomy\u2009\u00b1\u2009lymph nodes dissection between December 2014 and June 2020 at the PLA General Hospital First Medical Center and received necessary and regular follow\u2010up. We developed novel models using R software to predict factors that affect survival, such as progression\u2010free survival and overall survival. Then, the model was optimised by evaluating the prediction efficiency in multiple dimensions. Eight hundred and forty\u2010nine patients with EC were included in the study. Survival\u2010related 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\u2010cost, efficient, and clinically valuable in guiding treatment for EC patients.<\/jats:p>","DOI":"10.1111\/jcmm.70316","type":"journal-article","created":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T12:20:21Z","timestamp":1740140421000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Machine Learning Approach to Build and Evaluate a Molecular Prognostic Model for Endometrial Cancer Based on Tumour Microenvironment"],"prefix":"10.1111","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8460-7518","authenticated-orcid":false,"given":"Di","family":"Wu","sequence":"first","affiliation":[{"name":"School of Medicine Nankai University  Tianjin China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhifeng","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology The First Affiliated Center of Chinese People's Liberation Army (PLA) General Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingxia","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology The First Affiliated Center of Chinese People's Liberation Army (PLA) General Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Obstetrics and Gynecology The First Affiliated Center of Chinese People's Liberation Army (PLA) General Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanguang","family":"Meng","sequence":"additional","affiliation":[{"name":"School of Medicine Nankai University  Tianjin China"},{"name":"Department of Obstetrics and Gynecology The First Affiliated Center of Chinese People's Liberation Army (PLA) General Hospital  Beijing China"},{"name":"Department of Obstetrics and Gynecology The Seventh Medical Center of Chinese PLA General Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,2,21]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000033465"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.1136\/ijgc-2019-000822"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/jcmm.17697"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s43032-022-01052-4"},{"issue":"1","key":"e_1_2_8_7_1","first-page":"1","article-title":"Comprehensive Analysis of PHGDH for Predicting Prognosis and Immunotherapy Response in Patients With Endometrial Carcinoma","volume":"16","author":"Zhang H.","year":"2023","journal-title":"BMC Medical Genomics"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ygyno.2023.04.010"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.3892\/ol.2023.13700"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000033087"},{"key":"e_1_2_8_11_1","unstructured":"National Comprehensive Cancer Network \u201cUterine Neoplasms NCCN Clinical Practice Guidelines in Oncology \u201d(2022) www.nccn.org."},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.annonc.2022.05.009"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.esmoop.2022.100774"},{"key":"e_1_2_8_14_1","volume-title":"WHO Classification of Tumours of the Digestive System","author":"Bosman F. 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