[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-336745-105":59,"doc-detail-336745-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","integrated-machine-learning-algorithms-identify-kif15-as-a-potential-prognostic-biomarker-and-correlated-with-stemness-in-triple-negative-breast-cancer","Integrated machine learning algorithms identify KIF15 as a potential prognostic biomarker and correlated with stemness in triple-negative breast cancer","","Cancer stem cells drive self-renewal and tumor aggressiveness, contributing to metastasis, recurrence, and therapy resistance. This study integrates stemness gene sets and consensus clustering to classify triple-negative breast cancer samples, evaluates subtype differences in prognosis, tumor immune microenvironment, and chemotherapy sensitivity, and builds a stemness-related gene signature validated by random survival forest and tumor-sphere assays. KIF15 is identified as a core prognostic gene; its downregulation enhances TNBC stemness marker expression and promotes a higher stemness-associated risk profile.",{"@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-machine-learning-algorithms-identify-kif15-as-a-potential-prognostic-biomarker-and-correlated-with-stemness-in-triple-negative-breast-cancer/336745/",{"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-machine-learning-algorithms-identify-kif15-as-a-potential-prognostic-biomarker-and-correlated-with-stemness-in-triple-negative-breast-cancer/336745.png","ImageObject",300,407,{"name":92,"@type":93},"Miles","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-27","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"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 on TNBC?","Question",{"text":112,"@type":113},"To identify associations between cancer stem cell-related genes and triple-negative breast cancer development, focusing on prognosis-linked stemness regulators.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were TNBC samples classified in the analysis?",{"text":117,"@type":113},"StemChecker-derived stemness gene sets were used with a consensus clustering algorithm to unsupervisedly classify TNBC samples, then subtypes were compared for prognosis, tumor immune microenvironment, and chemotherapeutic sensitivity.",{"name":119,"@type":110,"acceptedAnswer":120},"What evidence supports KIF15 as a prognostic gene in TNBC?",{"text":121,"@type":113},"KIF15 emerged from stemness-associated gene signature analysis using random survival forest, was confirmed by LASSO and survival forest methods, and its downregulation promoted stemness and increased stem cell marker expression in TNBC cells.","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},336745,1790097633,{"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":34,"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},13056703019404,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nIntegrated machine learning algorithms identify KIF15  \nas a potential prognostic biomarker and correlated with stemness in triple‑negative breast cancer  \nQiaonan Guo1,3, Pengjun Qiu1,3, Kelun Pan1,3, Huikai Liang1, Zundong Liu2* & Jianqing Lin1*  \nCancer stem cells (CSCs) have the potential to self‑renew and induce cancer, which may contribute toa poor prognosis by enabling metastasis, recurrence, and therapy resistance. Hence, this study was performed to identify the association between CSC‑related genes and triple‑negative breast cancer (TNBC) development. Stemness gene sets were downloaded from StemChecker. Based on the online databases, a consensus clustering algorithm was conducted for unsupervised classification ofTNBC samples. The variations between subtypes were assessed with regard to prognosis, tumor immune microenvironment (TIME), and chemotherapeutic sensitivity. The stemness‑related gene signature was established and random survival forest analysis was employed to identify the core gene for validation experiments and tumor sphere formation assays. 499 patients with TNBC were classified into three subgroups and the Cluster 1 had a better OS than others. After that, WGCNA study was performed to identify genes important for Cluster 1 subtype. Out of all 8 modules, the subtype of Cluster 1 and the yellow module with 103 genes demonstrated the largest positive association. After that, a four‑gene stemness‑related signature was established. Based on the yellow module, the 39 potential pivotal genes were subjected to the random forest survival analysis to find out the gene that was relatively important for OS. KIF15 was confirmed as the targeted gene by LASSO and random survival forest analyses. In vitro experiments, the downregulation ofKIF15 promoted the stemnessofTNBC cells. The expression levels of stem cell markers Nanog, SOX2, and OCT4 were found to be elevated inTNBC cell lines after KIF15 inhibition. A stemness‑associated risk model was constructed to forecast the clinical outcomes ofTNBC patients. The downregulation of KIF15 expression in asubpopulation ofTNBC stem cells may promote stemness and possibly TNBC progression.  \nKeywords Triple-negative breast cancer (TNBC), Cancer stem cell (CSC), KIF15, Tumor microenvironment (TME), Prognosis, Risk model  \nBreast cancer (BC) is one of the most commonly diagnosed malignancies, responsible for approximately 31% of new cancer cases in women and is listed as the second cause of cancer-related deaths in women in the 2023 Cancer Statistics1. TNBC is a special subtype of BC that is histologically distinguished by the absence of human epidermal growth factor receptor 2 (HER-2), estrogen receptor (ER), and progesterone receptor (PR) . Current available treatments for TNBC include a mastectomy or breast-conserving procedure, chemotherapy, radiotherapy, immunotherapy, and other newly developed therapies2. Despite a variety of treatment strategies, the incidence of metastasis, recurrence, and drug resistance remains high in TNBC patients. Accumulating evidence indicates that numerous malignancies involving BC have populations of cells that resemble stem cells3. CSCs area dynamic subgroup of tumor cells that have the ability to self-renew, be pluripotent, and proliferate indefinitely4. Given their relative resistance to cytotoxic chemotherapy and radiotherapy, these BC stem cells (BCSCs) may  \n1Department of Breast and Thyroid Surgery, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China. 2Stem Cell Laboratory, Second Affiliated Hospital of Fujian Medical University, Quanzhou, China. 3These authors contributed equally: Qiaonan Guo, Pengjun Qiu and Kelun Pan.* email: [harryblood1989@126.com](harryblood1989@126.com); [ljq13905977336@163.com](ljq13905977336@163.com)  \n[www. nature.com/scientificreports/](www. nature.com/scientificreports/)  \nplay a vital role in drug resistance an","cbCaidgd0ARk8IW6","https://ap.wps.com/l/cbCaidgd0ARk8IW6","pdf",8558663,15,"English","# Introduction\n## Cancer stem cells and TNBC background\n## Tumor microenvironment and therapy resistance\n# Methods and Analysis\n## Stemness gene sets and consensus clustering\n## Prognosis, immune microenvironment, and sensitivity comparisons\n## Gene signature construction and validation\n# Results\n## Subgroup stratification and survival outcomes\n## WGCNA module association and pivotal gene discovery\n## KIF15 confirmation by LASSO and survival forest\n# Experimental Validation\n## KIF15 knockdown and stemness effects\n## Stemness marker changes and risk model construction","[{\"question\":\"What is the main objective of the study on TNBC?\",\"answer\":\"To identify associations between cancer stem cell-related genes and triple-negative breast cancer development, focusing on prognosis-linked stemness regulators.\"},{\"question\":\"How were TNBC samples classified in the analysis?\",\"answer\":\"StemChecker-derived stemness gene sets were used with a consensus clustering algorithm to unsupervisedly classify TNBC samples, then subtypes were compared for prognosis, tumor immune microenvironment, and chemotherapeutic sensitivity.\"},{\"question\":\"What evidence supports KIF15 as a prognostic gene in TNBC?\",\"answer\":\"KIF15 emerged from stemness-associated gene signature analysis using random survival forest, was confirmed by LASSO and survival forest methods, and its downregulation promoted stemness and increased stem cell marker expression in TNBC cells.\"}]","Integrated machine learning algorithms identify KIF15 as a potential prognostic biomarker and correlated with stemness in triple-negative breast cancer | PDF",1790016445,38]