[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128330-en":3,"doc-seo-128330-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128330,962085564549,"Genevieve","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Development of a Senescence-Related lncRNA Signature in Endometrial Cancer Based on Multiple Machine Learning Models","Senescence-related lncRNAs (srlncRNAs) are investigated for their roles in endometrial cancer (EC), where their prognostic and therapeutic value remains unclear. Using TCGA gene expression and clinical data together with CellAge srlncRNA resources, a co-expression network and multiple machine-learning workflows identify EC-associated prognostic srlncRNAs and build a predictive model. A nomogram integrating age, grade, and risk score improves outcome prediction, supported by GSEA and immune profiling. Drug-sensitivity analysis in patient-derived tumor organoids supports targeted therapeutic relevance.","TYPE Original Research PUBLISHED 27 November 2025 DOI 10.3389/fgene.2025.1687922  \nOPEN ACCESS  \nEDITED BY  \nDomenico Mallardo,  \nG. Pascale National Cancer Institute Foundation (IRCCS), Italy  \nREVIEWED BY  \nHongrong Wu,  \nFirst Affiliated Hospital of Shantou University Medical College, China  \nYanna Zhang,  \nUniversity of Electronic Science and Technology of China, China  \n*CORRESPONDENCE  \nYang Sun,  \n [sunyang@fjmu.edu.cn](sunyang@fjmu.edu.cn)  \n‡These authors have contributed equally to this work  \nRECEIVED 18 August 2025  \nREVISED 29 October 2025  \nACCEPTED 11 November 2025  \nPUBLISHED 27 November 2025  \nCITATION  \nLin J, Lei X, Li Y, Jiang X, Jiang F, Guo A, Cai X, Ye X and Sun Y (2025) Development of asenescence-related lncRNA signature in endometrial cancer based on multiple machine learning models.  \nFront. Genet. 16:1687922 .  \ndoi: 10.3389/fgene.2025.1687922  \nCOPYRIGHT  \n© 2025 Lin, Lei, Li, Jiang, Jiang, Guo, Cai, Ye and Sun. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nDevelopment of asenescence-related lncRNA signature in endometrial cancer based on multiple machine learning models  \nJie Lin 1‡, Xuemei Lei 1‡, Yanhong Li 1, Xin Jiang 2,3, Fengle Jiang 2,3, Aihua Guo 1, Xintong Cai 4, Xingming Ye 5 and Yang Sun 􀀁 1,5*  \n1Department of Gynecology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, China, 2Innovation Center for Cancer Research, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China, 3Fujian Key Laboratory of Advanced Technology for Cancer Screening and Early Diagnosis, Fuzhou, China, 4Department of Gynecology and Obstetrics, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China, 5Fujian Provincial Key Laboratory of Tumor Biotherapy, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian Province, China  \nBackground: Senescence-related lncRNAs (srlncRNA) mediate carcinogenesis in various malignancies. However, its roles in endometrial cancer (EC) remain unknown. Our research aims to construct a predictive srlncRNA model with prognostic and therapeutic significance in EC.  \nMethods: We first downloaded the gene expression and medical information from the TCGA, as well as senescence-related lncRNAs (srlncRNAs) from the CellAge databases. Then, a co-expression network of cell senescence-related mRNA−lncRNA was explored with R. Subsequently, we performed Cox and Lasso regression and machine learning analysis to identify srlncRNAs related to the prognosis of EC and built a predictive model. Continually, we drew a nomogram to improve its ability to predict prognosis. Further, GSEA was used to explore potential mechanisms. Differences in TME, immune infiltrating cells, and checkpoints of the two risk groups were compared using GSEA and CIBERSORT. Finally, the drug sensitivity of patient-derived tumor organoids (PDOs) was investigated.  \nResults: We first built a prognostic model based on seven srlncRNAs (AL121906.2, AP002761.4, BX322234.1, LINC00662, LINC00908, VIM-AS1, and ZNF236-DT) . The model, which was screened by machine learning, functioned well in three sets with good stability and accuracy. Furthermore, the nomogram based on age, grade, and risk scores could precisely predict the prognosis of EC patients. The AUC of risk scores was highest compared to other clinical parameters (AUC risk score = 0.769, AUCage = 0 . 615, and AUC grade = 0 . 681) . This srlncRNAs were enriched in the cell cycle, certain malignant tumors, and cancer-associated regulatory path","cbCaifTb5XXGxu5K","https://ap.wps.com/l/cbCaifTb5XXGxu5K","pdf",5489650,3,1,15,"English","en",105,"# Background\n# Methods\n## Data sources and feature construction\n## Survival modeling and predictive signature building\n## Nomogram and functional enrichment\n## Immune infiltration, checkpoints, and drug sensitivity\n# Results\n## Seven-srlncRNA prognostic model\n## Nomogram performance\n## Pathways, immune microenvironment, and therapy implications\n## Drug sensitivity in patient-derived tumor organoids\n# Conclusion","[{\"question\":\"What is the main goal of the study in endometrial cancer?\",\"answer\":\"To construct a prognostic srlncRNA model for endometrial cancer and evaluate its potential therapeutic implications.\"},{\"question\":\"How were the candidate senescence-related lncRNAs identified?\",\"answer\":\"The workflow downloads EC expression and clinical information from TCGA and srlncRNAs from CellAge, then builds a co-expression network and uses Cox, Lasso regression, and machine-learning analysis to select prognostic lncRNAs.\"},{\"question\":\"What does the final prognostic signature include and how is it used?\",\"answer\":\"The signature contains seven srlncRNAs (AL121906.2, AP002761.4, BX322234.1, LINC00662, LINC00908, VIM-AS1, and ZNF236-DT). It stratifies patients into risk groups and supports prognosis prediction, including via a nomogram using age, grade, and risk scores.\"}]","Development of a Senescence-Related lncRNA Signature in Endometrial Cancer Based on Multiple Machine Learning Models | PDF",1785946869,38,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"development-of-a-senescence-related-lncrna-signature-in-endometrial-cancer-based-on-multiple-machine-learning-models","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/development-of-a-senescence-related-lncrna-signature-in-endometrial-cancer-based-on-multiple-machine-learning-models/128330/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-28","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main goal of the study in endometrial cancer?","Question",{"text":76,"@type":77},"To construct a prognostic srlncRNA model for endometrial cancer and evaluate its potential therapeutic implications.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the candidate senescence-related lncRNAs identified?",{"text":81,"@type":77},"The workflow downloads EC expression and clinical information from TCGA and srlncRNAs from CellAge, then builds a co-expression network and uses Cox, Lasso regression, and machine-learning analysis to select prognostic lncRNAs.",{"name":83,"@type":74,"acceptedAnswer":84},"What does the final prognostic signature include and how is it used?",{"text":85,"@type":77},"The signature contains seven srlncRNAs (AL121906.2, AP002761.4, BX322234.1, LINC00662, LINC00908, VIM-AS1, and ZNF236-DT). 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