[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-353043-105":59,"doc-detail-353043-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","problem-solving-protocol-identification-and-characterization-of-lncrna-stemness-immune-regulatory-patterns","Problem Solving Protocol - Identification and characterization of lncRNA-stemness-immune regulatory patterns","","Long noncoding RNAs (lncRNAs) regulate stemness signature genes (SSGs) and tumor immunity, shaping the tumor microenvironment and antitumor immune responses. Cancer stem cell traits relate to immune evasion and therapeutic resistance, motivating pan-cancer characterization of SSG, lncRNA, and immune interactions. An integrative network-and-Bayesian framework identifies core regulatory triplets (STEM-LncCRTs) linking an lncRNA, an SSG, and an immune gene, enabling subtype discrimination, immune infiltration association, and improved prognostic and immunotherapy response prediction.",{"@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/problem-solving-protocol-identification-and-characterization-of-lncrna-stemness-immune-regulatory-patterns/353043/",{"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/problem-solving-protocol-identification-and-characterization-of-lncrna-stemness-immune-regulatory-patterns/353043.png","ImageObject",300,407,{"name":92,"@type":93},"supergirl","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-24","2026-09-22",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main objective of the study?","Question",{"text":112,"@type":113},"To systematically identify and characterize pan-cancer regulatory patterns connecting stemness signature genes, lncRNAs, and tumor immunity, and to derive predictive biomarkers for prognosis and immunotherapy response.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are core regulatory triplets (STEM-LncCRTs) identified?",{"text":117,"@type":113},"The study uses an integrative analytical framework combining network-based modeling with Bayesian network inference to extract triplets consisting of an lncRNA, an SSG, and an immune gene.",{"name":119,"@type":110,"acceptedAnswer":120},"What evidence supports the clinical relevance of STEM-LncCRTs?",{"text":121,"@type":113},"A specific triplet (ATAD5/PRR11-AS1/SKP2) shows favorable prognostic potential across multiple cancers, and STEM-LncCRTs validated across three immunotherapy cohorts with four machine learning algorithms for predicting immune checkpoint inhibitor responses.","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},353043,1790164558,{"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":81,"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},962088121634,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Briefings in Bioinformatics, 2026, 27, bbag287 [https://doi.org/10.1093/bib/bbag287](https://doi.org/10.1093/bib/bbag287)  \nPublished: 4 June 2026  \nProblem Solving Protocol  \nIdentification and characterization of lncRNA-stemness-immune  \nregulatory patterns  \nZhipeng Qian1 ,‡, Jiaqi Yin2 ,‡, Chunlong Zhang2 , Guohua Wang2 , 3 , Chunyu Wang3 , Yang Li2 , *, Yuming Zhao1 , 2 , *  \n1 College of Life Sciences, Northeast Forestry University, No. 26 Hexing Road, Xiangfang District, Harbin, Heilongjiang 150040, China  \n2 College of Computer and Control Engineering, Northeast Forestry University, No. 26 Hexing Road, Xiangfang District, Harbin, Heilongjiang 150040, China  \n3 School of Computer Science and Technology, Harbin Institute of Technology, No. 92 Xidazhi Street, Nangang District, Harbin, Heilongjiang 150001, China  \n*Corresponding authors. Yang Li, College of Computer and Control Engineering, Northeast Forestry University, No. 26 Hexing Road, Xiangfang District, Harbin, Heilongjiang 150040, China. E-mail: [yli@nefu.edu.cn](yli@nefu.edu.cn); Yuming Zhao, College of Computer and Control Engineering, Northeast Forestry University, No. 26 Hexing Road, Xiangfang District, Harbin, Heilongjiang 150040, China.  \nE-mail: [zym@nefu.edu.cn](zym@nefu.edu.cn)  \n‡Zhipeng Qian and Jiaqi Yin contributed equally to this work.  \nAbstract  \nLong noncoding RNAs (lncRNAs) play critical roles in regulating stemness signature genes (SSGs) and tumor immunity, thereby shaping the tumor microenvironment and antitumor immune responses. Increasing evidence suggests that cancer stem cell traits are closely associated with immune evasion and therapeutic resistance, underscoring the need to systematically characterize the pan-cancer interplay among SSGs, lncRNAs, and tumor immunity. Here, we developed an integrative analytical framework that combines network-based modeling with Bayesian network inference to identify core regulatory triplets (STEM-LncCRTs), each consisting of an lncRNA, an SSG, and an immune gene. We demonstrate that specific stemness-related lncRNAs can distinguish cancer subtypes, and that common stemnessrelated lncRNAs correlate significantly with immune cell infiltration. Notably, the ATAD5/PRR11-AS1/SKP2 triplet exhibits favorable prognostic potential across multiple cancers and consistently outperforms individual gene markers in predicting 1-, 3-, and 5-year overall survival. Furthermore, using four machine learning algorithms across three independent immunotherapy cohorts, we validate the predictive value of STEM-LncCRTs for immune checkpoint inhibitor response. Importantly, integrating STEM-LncCRTs with tumor mutation burden further improves predictive accuracy. Collectively, this study provides a systems-level view of stemness-related lncRNA regulation in tumor immunity and offers practical biomarkers for predicting immunotherapy efficacy.  \nGraphical abstract  \nKeywords Bayesian network inference, core regulatory triplets, stemness-related lncRNAs, prognostic, immunotherapy  \n• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •   \nReceived: February 12, 2026. Revised: March 29, 2026. Accepted: May 11, 2026  \n© The Author(s) 2026. Published by Oxford University Press.  \nThis is an Open Access article distributed under the terms of the Creative Commons Attribution License ([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.  \n2 | Briefings in Bioinformatics, 2026, Vol. 27, Issue 3  \nIntroduction  \nCancer currently ranks as the second leading cause of death worldwide, following cardiovascular diseases [1] . Despite continuous advances in treatment strategies, the mortality rates of many cancer types remain alarmingly high [2] .","cbCaiaCN3yzY5BhF","https://ap.wps.com/l/cbCaiaCN3yzY5BhF","pdf",2806840,14,"English","# Abstract\n# Introduction\n## Cancer, stemness, and immune regulation\n## lncRNAs and mechanisms in tumor biology","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To systematically identify and characterize pan-cancer regulatory patterns connecting stemness signature genes, lncRNAs, and tumor immunity, and to derive predictive biomarkers for prognosis and immunotherapy response.\"},{\"question\":\"How are core regulatory triplets (STEM-LncCRTs) identified?\",\"answer\":\"The study uses an integrative analytical framework combining network-based modeling with Bayesian network inference to extract triplets consisting of an lncRNA, an SSG, and an immune gene.\"},{\"question\":\"What evidence supports the clinical relevance of STEM-LncCRTs?\",\"answer\":\"A specific triplet (ATAD5/PRR11-AS1/SKP2) shows favorable prognostic potential across multiple cancers, and STEM-LncCRTs validated across three immunotherapy cohorts with four machine learning algorithms for predicting immune checkpoint inhibitor responses.\"}]","Problem Solving Protocol - Identification and characterization of lncRNA-stemness-immune regulatory patterns | PDF",1790102706,35]