[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126980-en":3,"doc-seo-126980-105":30,"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126980,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","mtPCDI - a machine learning-based prognostic model for prostate cancer recurrence","This research formulates a prognostic model to forecast prostate cancer recurrence by linking mitochondrial function with programmed cell death (PCD). Four gene expression datasets from TCGA and GEO are analyzed using univariate Cox regression to identify recurrence-associated genes related to mitochondrial function and PCD. Multiple machine learning methods build an optimal predictive framework. The resulting mtPCDI index stratifies patients: lower mtPCDI corresponds to higher immune activity and better recurrence outcomes.","TYPE Original Research PUBLISHED 04 September 2024 DOI 10.3389/fgene.2024.1430565  \nOPEN ACCESS  \nEDITED BY  \nXiangqian Guo,  \nHenan University, China  \nREVIEWED BY  \nJiateng Zhong,  \nXinxiang Medical University, China Johnkennedy Nnodim, Imo State University, Nigeria Binglin Wang,  \nHenan University, China  \n*CORRESPONDENCE  \nYong Fan,  \n [15665809617@wo.cn](15665809617@wo.cn)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 10 May 2024  \nACCEPTED 22 August 2024  \nPUBLISHED 04 September 2024  \nCITATION  \nCheng G, Xu J, Wang H, Chen J, Huang L, Qian ZR and Fan Y (2024) mtPCDI: a machine learning-based prognostic model for prostate cancer recurrence.  \nFront. Genet. 15:1430565 .  \ndoi: 10.3389/fgene.2024.1430565  \nCOPYRIGHT  \n© 2024 Cheng, Xu, Wang, Chen, Huang, Qian and Fan. 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.  \nmtPCDI: a machine  \nlearning-based prognostic model for prostate cancer recurrence  \nGuoliang Cheng 1†, Junrong Xu 1†, Honghua Wang 1,  \nJingzhao Chen 2, Liwei Huang 2, Zhi Rong Qian 2 and Yong Fan 1* 1Department of Urology Surgery, The Fourth People’s Hospital of Jinan, Jinan, Shandong, China, 2Beidou Precision Medicine Institute, Guangzhou, China  \nBackground: This research seeks to formulate a prognostic model for forecasting prostate cancer recurrence by examining the interaction between mitochondrial function and programmed cell death (PCD) .  \nMethods: The research involved analyzing four gene expression datasets from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) using univariate Cox regression. These analyses identified genes linked with mitochondrial function and PCD that correlate with recurrence prognosis. Various machine learning algorithms were then employed to construct an optimal predictive model.  \nResults: A key outcome was the creation of a mitochondrial-related programmed cell death index (mtPCDI), which effectively predicts the prognosis of prostate cancer patients. It was observed that individuals with lower mtPCDI exhibited higher immune activity, correlating with better recurrence outcomes.  \nConclusion: The study demonstrates that mtPCDI can be used for personalized risk assessment and therapeutic decision-making, highlighting its clinical significance and providing insights into the biological processes affecting prostate cancer recurrence.  \nKEYWORDS  \nmachine learning, targeted cancer therapy, prostate cancer, programmed cell death, mitochondrial activity, tumor immune microenvironment  \n1 Introduction  \nProstate cancer, a prevalent cancer affecting men, especially in older age categories, originates from the prostate gland and often progresses slowly, exhibiting few noticeable symptoms (Siegel et al., 2023) . The development of this cancer is influenced by genetic, environmental, and lifestyle factors. Early detection is crucial for managing the disease and is primarily achieved by prostate-specific antigen (PSA) testing, which is also vital for tracking recurrence of the disease after treatment (Fenton et al., 2018) . Treatment alternatives include active monitoring, surgery, radiotherapy, androgen deprivation therapy, chemotherapeutic treatments, targeted therapies, and immunotherapies. Recent advances in treatments and a deeper understanding of the disease’s molecular mechanisms have significantly improved patient outcomes (Nuhn et al., 2019; Subudhi et al., 2020; Rehman et al., 2023; Xie et al., 2023) . However, outcomes can vary depending on the molecular properties of the tumors and individual patient differences.  \nFrontier","cbCaierSBiuaHntl","https://ap.wps.com/l/cbCaierSBiuaHntl","pdf",6795542,1,17,"English","en",105,"# Introduction\n# Methods\n# Results\n# Conclusion","[{\"question\":\"What clinical problem does mtPCDI address?\",\"answer\":\"It provides a prognostic framework to forecast prostate cancer recurrence and support personalized risk assessment.\"},{\"question\":\"How was the prognostic model constructed?\",\"answer\":\"Four TCGA and GEO gene-expression datasets were analyzed with univariate Cox regression, then machine learning algorithms were used to build an optimal predictive model.\"},{\"question\":\"What does a lower mtPCDI score indicate?\",\"answer\":\"Patients with lower mtPCDI show higher immune activity and are associated with better recurrence outcomes.\"}]","mtPCDI - a machine learning-based prognostic model for prostate cancer recurrence | PDF",1785936017,43,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"mtpcdi-a-machine-learning-based-prognostic-model-for-prostate-cancer-recurrence","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/mtpcdi-a-machine-learning-based-prognostic-model-for-prostate-cancer-recurrence/126980/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","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 clinical problem does mtPCDI address?","Question",{"text":76,"@type":77},"It provides a prognostic framework to forecast prostate cancer recurrence and support personalized risk assessment.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was the prognostic model constructed?",{"text":81,"@type":77},"Four TCGA and GEO gene-expression datasets were analyzed with univariate Cox regression, then machine learning algorithms were used to build an optimal predictive model.",{"name":83,"@type":74,"acceptedAnswer":84},"What does a lower mtPCDI score indicate?",{"text":85,"@type":77},"Patients with lower mtPCDI show higher immune activity and are associated with better recurrence outcomes.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]