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This study integrates DNA methylation, gene expression, and metabolic profiles from the same individuals using the unsupervised MOFA framework to characterize normal, malignant, and aggressive prostate tissue. Distinct pathways linked to aggressive disease include zinc metabolism, cell cycle regulation, smooth muscle architecture, immune activation, and tissue morphology. Key metabolites highlight TCA cycle, amino acid metabolism, and lipid pathways, with factor-associated CpGs co-enriched for SP1 and CTCFL regions.",{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/integrated-multi-omics-profiling-uncovers-the-epigenetic-transcriptional-and-metabolic-landscape-of-prostate-cancer-progression/350659/",{"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/integrated-multi-omics-profiling-uncovers-the-epigenetic-transcriptional-and-metabolic-landscape-of-prostate-cancer-progression/350659.png","ImageObject",300,407,{"name":92,"@type":93},"kopisore","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-26","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What multi-omics data types were integrated in the study?","Question",{"text":112,"@type":113},"The study integrated DNA methylation, gene expression (transcriptomics), and metabolic profiles derived from the same individuals using MOFA.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which molecular features were linked to aggressive prostate cancer progression?",{"text":117,"@type":113},"Aggressive disease was associated with pathways involving zinc metabolism, cell cycle regulation, smooth muscle architecture, immune activation, and tissue morphology, supported by TCA cycle, amino acid metabolism, and lipid pathway metabolites.",{"name":119,"@type":110,"acceptedAnswer":120},"How were the proposed molecular signatures validated for clinical relevance?",{"text":121,"@type":113},"Signatures were validated in the TCGA cohort and showed significant predictive value for disease recurrence.","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},350659,1790436664,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":14,"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":145},962090880963,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Pedersen et al. BMC Cancer (2026) 26:967 BMC Cancer  \n[https://doi.org/10.1186/s12885-026-16232-7](https://doi.org/10.1186/s12885-026-16232-7)  \nRESEARCH Open Access  \nIntegrated multi-omics profiling uncovers the epigenetic, transcriptional, and metabolic landscape of prostate cancer progression  \nChristine Aaserød Pedersen 1†, Maximilian Wess1,7*†, Maria K. Andersen 1,2, Elise Midtbust1,2, Abhibhav Sharma1, Thomas Fleischer3, Morten Beck Rye4,5,6 and May-Britt Tessem1,2*  \nAbstract  \nA comprehensive understanding of the underlying molecular mechanisms of prostate cancer is essential for the development of precise diagnostic biomarkers. In this study, we applied the unsupervised multi-omics factor analysis framework (MOFA) to integrate DNA methylation, gene expression, and metabolic profiles derived from the same individuals, aiming to characterize the biological landscape of normal, malignant, and aggressive prostate tissue. Our analysis identified distinct molecular pathways associated with aggressive disease, specifically those involved in zinc metabolism, cell cycle regulation, smooth muscle architecture, immune activation, and tissue morphology. Key metabolites within the TCA cycle, amino acid metabolism, and lipid pathways were central to these signatures. Furthermore, we observed a consistent co-enrichment of SP1 and CTCFL binding regions among factor-associated CpGs, suggesting a model of global epigenetic reprogramming. These findings indicate a novel interplay between Polycomb deregulation, CTCFL-mediated chromatin remodeling, and SP1-driven transcriptional activation in shaping the prostate cancer epigenome. Apart from immune activation, the identified molecular signatures were validated in the TCGA cohort and demonstrated significant predictive value for disease recurrence. Overall, these results underscore the power of multi-omics integration in providing a holistic understanding of prostate cancer biology and its potential for clinical translation into prognostic biomarkers.  \nKeywords Prostate cancer, Multi-omics, Multi-omics factor analysis framework (MOFA), Transcriptomics, Metabolomics, DNA methylation  \n†Christine Aaserød Pedersen and Maximilian Wess contributed equally to this work.  \n*Correspondence: Maximilian Wess [maximilian.wess@ntnu. no](maximilian.wess@ntnu. no)[ ](maximilian.wess@ntnu. no)May-Britt Tessem [may-britt.tessem@ntnu. no](may-britt.tessem@ntnu. no)  \n1Department of Circulation and Medical Imaging, NTNU, Trondheim, Norway  \n2Clinic of Surgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway  \n3Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway  \n4Department of Clinical and Molecular Medicine, NTNU, Trondheim, Norway  \n5Clinic of Laboratory Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway  \n6BioCore-Bioinformatics Core Facility, NTNU – Norwegian University of Science and Technology, Trondheim, Norway  \n7ELIXIR Norway, NTNU – Norwegian University of Science and Technology, Trondheim, Norway  \n© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0","cbCaiqbCeHVng4dT","https://ap.wps.com/l/cbCaiqbCeHVng4dT","pdf",3356251,17,"English","# Abstract\n## Research approach and data integration\n## Molecular pathways and biomarkers","[{\"question\":\"What multi-omics data types were integrated in the study?\",\"answer\":\"The study integrated DNA methylation, gene expression (transcriptomics), and metabolic profiles derived from the same individuals using MOFA.\"},{\"question\":\"Which molecular features were linked to aggressive prostate cancer progression?\",\"answer\":\"Aggressive disease was associated with pathways involving zinc metabolism, cell cycle regulation, smooth muscle architecture, immune activation, and tissue morphology, supported by TCA cycle, amino acid metabolism, and lipid pathway metabolites.\"},{\"question\":\"How were the proposed molecular signatures validated for clinical relevance?\",\"answer\":\"Signatures were validated in the TCGA cohort and showed significant predictive value for disease recurrence.\"}]","Integrated multi-omics profiling uncovers the epigenetic, transcriptional, and metabolic landscape of prostate cancer progression | PDF",1790090462,43]