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This work integrates ssGSEA, WGCNA, and multiple machine-learning methods to identify tumor stemness key genes (TSKGs), then builds a prognostic framework (TSRGPM). Using PCA, Cox regression, and ROC analyses, tumor stemness risk scores (TSRS) stratify patients as an independent prognostic indicator and associate with lymph node metastasis. CIBERSORT-based immune infiltration further links TSRS with monocytes, while CDC25A is singled out. Bioinformatics and in vitro validation support CDC25A’s role in gastric tumor cell proliferation, DNA replication, and modulating tumor stemness, informing personalized management and potential therapeutic targets.",{"@graph":69,"@context":126},[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/machine-learning-for-identifying-tumor-stemness-genes-and-developing-prognostic-model-in-gastric-cancer/330479/",{"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/machine-learning-for-identifying-tumor-stemness-genes-and-developing-prognostic-model-in-gastric-cancer/330479.png","ImageObject",300,407,{"name":92,"@type":93},"Emma Wilson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118,122],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of the study in gastric cancer?","Question",{"text":112,"@type":113},"To identify tumor stemness key genes using integrated computational methods and to develop a prognostic model based on tumor stemness risk scores.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is the prognostic model constructed and evaluated?",{"text":117,"@type":113},"The study uses ssGSEA and WGCNA to screen genes, applies machine-learning selections to obtain tumor stemness key genes, and then builds TSRGPM. PCA, Cox regression, and ROC analyses evaluate the predictive performance of TSRS.",{"name":119,"@type":110,"acceptedAnswer":120},"What is the relationship between TSRS and clinical or biological features?",{"text":121,"@type":113},"TSRS correlates significantly with lymph node metastasis in gastric cancer. It also associates with immune infiltration patterns, including monocytes, based on CIBERSORT analysis.",{"name":123,"@type":110,"acceptedAnswer":124},"Why is CDC25A highlighted, and how is it validated?",{"text":125,"@type":113},"After further scrutiny of tumor stemness risk genes, CDC25A is selected for detailed investigation. Bioinformatics suggests it contributes to malignant phenotypes and influences proliferation and DNA replication, and in vitro experiments validate the computational findings regarding its role in modulating tumor stemness.","https://schema.org",{"og:url":83,"og:type":128,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":130,"canonical":83},"index,follow",{"doc_id":132,"site_id":62},330479,1790193385,{"code":4,"msg":5,"data":135},{"doc_id":132,"user_id":136,"nickname":92,"user_avatar":137,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":138,"file_id":139,"file_url":140,"file_type":141,"file_size":142,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":143,"language":144,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":145,"faqs":146,"seo_title":147,"seo_description":67,"update_tm":148,"read_time":149,"preview_page_count":4},3848291630094,"https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","[www.aging-us.com](www.aging-us.com) AGING 2024, Vol. 16, No. 7  \nResearch Paper  \nMachine learning for identifying tumor stemness genes and developing prognostic model in gastric cancer  \nGuo-Xing Li1,*, Yun-Peng Chen2,*, You-Yang Hu2,*, Wen-Jing Zhao1, Yun-Yan Lu1, Fu-Jian Wan3, Zhi-Jun Wu4, Xiang-Qian Wang1, Qi-Ying Yu1  \n1 Department of Oncology and Central Laboratory, Tumor Hospital Affiliated to Nantong University, Nantong, Jiangsu 226361, P.R. China  \n2 Department of Oncology, The Affiliated Hospital of Nantong University, Nantong, Jiangsu 226361, P. R. China 3 Institute of Biology and Medicine, College of Life and Health Sciences, Wuhan University of Science and Technology, Wuhan, Hubei 430081, P.R. China  \n4 Department of Oncology, Nantong Hospital of Traditional Chinese Medicine, Nantong, Jiangsu 226361, P. R. China * Equal contribution  \nCorrespondence to: Qi-Ying Yu, Zhi-Jun Wu, Xiang-Qian Wang; [email:](email: wust202018601003@163.com)[ ](email: wust202018601003@163.com)[wust202018601003@163.com](email: wust202018601003@163.com), [https://orcid.org/0009-](https://orcid.org/0009-)[ ](https://orcid.org/0009-)0008-8651-9335; [ntzlyyflk@163.com](ntzlyyflk@163.com), [https://orcid.org/0000-0001-6046-6767](https://orcid.org/0000-0001-6046-6767); [wangxiangqian01@126.com](wangxiangqian01@126.com), [https://orcid.org/0009-0005-0542-7892](https://orcid.org/0009-0005-0542-7892)  \nKeywords: gastric cancer, cancer stemness, machine learning, ssGSEA  \nReceived: October 31, 2023 Accepted: March 13, 2024 Published: April 12, 2024  \nCopyright: © 2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nABSTRACT  \nGastric cancer presents a formidable challenge, marked by its debilitating nature and often dire prognosis. Emerging evidence underscores the pivotal role of tumor stem cells in exacerbating treatment resistance and fueling disease recurrence in gastric cancer. Thus, the identification of genes contributing to tumor stemness assumes paramount importance. Employing a comprehensive approach encompassing ssGSEA, WGCNA, and various machine learning algorithms, this study endeavors to delineate tumor stemness key genes (TSKGs). Subsequently, these genes were harnessed to construct a prognostic model, termed the Tumor Stemness Risk Genes Prognostic Model (TSRGPM) . Through PCA, Cox regression analysis and ROC curve analysis, the efficacy of Tumor Stemness Risk Scores (TSRS) in stratifying patient risk profiles was underscored, affirming its ability asan independent prognostic indicator. Notably, the TSRS exhibited a significant correlation with lymph node metastasis in gastric cancer. Furthermore, leveraging algorithms such as CIBERSORT to dissect immune infiltration patterns revealed a notable association between TSRS and monocytes and other cell. Subsequent scrutiny of tumor stemness risk genes (TSRGs) culminated in the identification of CDC25A for detailed investigation. Bioinformatics analyses unveil CDC25A’s implication in driving the malignant phenotype of tumors, with a discernible impact on cell proliferation and DNA replication in gastric cancer. Noteworthy validation through in vitro experiments corroborated the bioinformatics findings, elucidating the pivotal role of CDC25A expression in modulating tumor stemness in gastric cancer. In summation, the established and validated TSRGPM holds promise in prognostication and delineation of potential therapeutic targets, thus heralding a pivotal stride towards personalized management of this malignancy.  \nINTRODUCTION  \nGastric cancer stands out for its high morbidity and mortality rates among malignancies [1, 2] . Despite  \nadvancements in stomach cancer therapies in recent decades, current treatment modalities often fall short of achieving optimal outcomes, presenting ","cbCaifG138dfAdJm","https://ap.wps.com/l/cbCaifG138dfAdJm","pdf",3856400,23,"English","# ABSTRACT\n## INTRODUCTION","[{\"question\":\"What is the main goal of the study in gastric cancer?\",\"answer\":\"To identify tumor stemness key genes using integrated computational methods and to develop a prognostic model based on tumor stemness risk scores.\"},{\"question\":\"How is the prognostic model constructed and evaluated?\",\"answer\":\"The study uses ssGSEA and WGCNA to screen genes, applies machine-learning selections to obtain tumor stemness key genes, and then builds TSRGPM. PCA, Cox regression, and ROC analyses evaluate the predictive performance of TSRS.\"},{\"question\":\"What is the relationship between TSRS and clinical or biological features?\",\"answer\":\"TSRS correlates significantly with lymph node metastasis in gastric cancer. It also associates with immune infiltration patterns, including monocytes, based on CIBERSORT analysis.\"},{\"question\":\"Why is CDC25A highlighted, and how is it validated?\",\"answer\":\"After further scrutiny of tumor stemness risk genes, CDC25A is selected for detailed investigation. Bioinformatics suggests it contributes to malignant phenotypes and influences proliferation and DNA replication, and in vitro experiments validate the computational findings regarding its role in modulating tumor stemness.\"}]","Machine learning for identifying tumor stemness genes and developing prognostic model in gastric cancer | PDF",1789989115,58]