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Dysregulation of N4-acetylcytidine (ac4C) acetylation has been linked to aggressive progression in multiple cancers, yet shared epitranscriptomic regulatory patterns across cancer types remain unclear. Using 88 ac4C epitranscriptome datasets, the study builds an interpretable ac4C pan-cancer model and identifies common patterns tied to 3′ UTR GC content and internal 3′ UTR splicing, plus candidate ac4C-mediated genes associated with poor prognosis.",{"@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/integrative-interpretable-learning-reveals-shared-patterns-of-epitranscriptomic-regulation-across-multiple-cancer-types/349454/",{"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/integrative-interpretable-learning-reveals-shared-patterns-of-epitranscriptomic-regulation-across-multiple-cancer-types/349454.png","ImageObject",300,407,{"name":92,"@type":93},"Fans","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-23","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 is the main purpose of the study on ac4C epitranscriptomes?","Question",{"text":112,"@type":113},"To determine whether diverse cancer types share common epitranscriptomic regulatory patterns for N4-acetylcytidine (ac4C) acetylation, and to uncover interpretable associations across cancers.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How did the authors build their ac4C pan-cancer model?",{"text":117,"@type":113},"They leveraged 88 ac4C epitranscriptome datasets spanning multiple cancer types and normal conditions, integrating sequence information with curated genome-derived knowledge using a deep learning transformer architecture.",{"name":119,"@type":110,"acceptedAnswer":120},"What biological patterns and candidate genes were identified?",{"text":121,"@type":113},"The interpretability analysis highlighted shared dysregulated ac4C patterns, especially linked to low GC-content regions of the 3′ UTR and internal 3′ UTR splicing, and proposed candidate ac4C-mediated genes including SMARCD1, SENP5, and RNF207 associated with poor clinical prognosis.","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},349454,1790196979,{"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":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},5909892330395,"https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8","Yin et al. BMC Biology (2026) 24:163  \n[https://doi.org/10.1186/s12915-026-02628-1](https://doi.org/10.1186/s12915-026-02628-1)  \nBMC Biology  \n RESEARCH Open Access  \nIntegrative interpretable learning reveals  \nshared patterns of epitranscriptomic regulation across multiple cancer types  \nXiangyu Yin1,3,4†, Gang Tu3,5†, Xuan Wang3,5†, Yuqi Liu1, Yue Wang6, Jiongming Ma3,5, XiaoXuan Yu1*, Jia Meng2,3,5* and Bowen Song1*  \nAbstract  \nBackground Cancer is a complex set of diseases caused by the dysregulation of cell proliferation, differentiation, and apoptosis, ultimately leading to malignant tumor development and metastasis. Recent studies have revealed that dysregulation of N4-acetylcytidine (ac4C) acetylation is associated with enhanced metastatic potential and tumor progression in various cancer types. However, it remains unclear whether diverse cancer types share common epitranscriptomic regulatory patterns or engage in interconnected networks.  \nResults In this study, leveraging an extensive collection of 88 ac4C epitranscriptome datasets profiled across multiple cancer types and normal conditions, we developed the first ac4C pan-cancer model by integrating a diverse set of sequence and curated genome-derived knowledge, based on a combined deep learning-powered transformer architecture. Our interpretable analysis uncovered, for the first time, the shared epitranscriptomic patterns and associations of dysregulated ac4C across different cancers, particularly associated with low GC-content regions of the 3′ UTRand internal 3′ UTR splicing. Furthermore, we discovered a set of candidate ac4C-mediated genes that may function in cancers through epitranscriptomic regulation, including three novel ac4C-mediated candidates (SMARCD1, SENP5, RNF207) that exhibit consistently dysregulated ac4C levels, expression patterns, and are associated with poor clinical prognosis across different cancer types.  \nConclusions Taken together, our findings highlight the importance of a comprehensive characterization of ac4C epitranscriptome in pan-cancer landscape, with potential implications for developing RNA modification-based biomarkers and therapeutic strategies.  \nKeywords N4-acetylcytidine (ac4C), Epitranscriptomic regulation, Genomic features, Interpretable analysis  \n†Xiangyu Yin, Gang Tu, and Xuan Wang share equal contribution.  \n*Correspondence:  \nXiaoXuan Yu [xxyu@njucm.edu.cn](xxyu@njucm.edu.cn)[ ](xxyu@njucm.edu.cn)Jia Meng [jia.meng@xjtlu.edu.cn](jia.meng@xjtlu.edu.cn)[ ](jia.meng@xjtlu.edu.cn)Bowen Song [bowen.song@njucm.edu.cn](bowen.song@njucm.edu.cn)  \nFull list of author information is available at the end of the article  \nBackground  \nAdvances in cancer research have substantially enhanced our understanding of tumor biology, particularly through elucidating genetic and epigenetic alterations in oncogenes, which reveal the intricate molecular mechanisms driving tumorigenesis [1–4]. The epitranscriptome, also referred to as RNA modification (RM), has emerged asa critical regulatory layer in diverse cellular functions [5, 6], including cancer biology [7–10]. Recently, studies have highlighted the emerging significance of N4-acetylcytidine (ac4C) acetylation in mammalian development  \n© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, 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 you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. 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 arti","cbCaiut9BoPWRFRc","https://ap.wps.com/l/cbCaiut9BoPWRFRc","pdf",2447970,18,"English","# Abstract\n## Background\n## Results\n## Conclusions\n## Keywords","[{\"question\":\"What is the main purpose of the study on ac4C epitranscriptomes?\",\"answer\":\"To determine whether diverse cancer types share common epitranscriptomic regulatory patterns for N4-acetylcytidine (ac4C) acetylation, and to uncover interpretable associations across cancers.\"},{\"question\":\"How did the authors build their ac4C pan-cancer model?\",\"answer\":\"They leveraged 88 ac4C epitranscriptome datasets spanning multiple cancer types and normal conditions, integrating sequence information with curated genome-derived knowledge using a deep learning transformer architecture.\"},{\"question\":\"What biological patterns and candidate genes were identified?\",\"answer\":\"The interpretability analysis highlighted shared dysregulated ac4C patterns, especially linked to low GC-content regions of the 3′ UTR and internal 3′ UTR splicing, and proposed candidate ac4C-mediated genes including SMARCD1, SENP5, and RNF207 associated with poor clinical prognosis.\"}]","Integrative interpretable learning reveals shared patterns of epitranscriptomic regulation across multiple cancer types | PDF",1790083391,45]