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Transcriptome and single-cell sequencing data were integrated from TCGA, CGGA, and GEO, and apoptosis-related differential genes were identified using limma. An apoptosis-related gene prognostic model (AS) was built via univariate Cox analysis with machine-learning optimization and validated with bioinformatics tools. Results indicate distinct expression patterns between tumor and adjacent tissues and a model that predicts outcomes across datasets while linking HSPB1 to GBM prognosis and the tumor immune microenvironment.",{"@graph":69,"@context":121},[70,84,104],{"@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/exploration-of-the-prognostic-role-of-apoptosis-related-genes-in-glioblastoma/438153/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":98,"encodingFormat":97,"isAccessibleForFree":99,"interactionStatistic":100},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/exploration-of-the-prognostic-role-of-apoptosis-related-genes-in-glioblastoma/438153.png","ImageObject",300,407,{"name":92,"@type":93},"WPS_1790064749","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-09-29",true,{"@type":101,"interactionType":102,"userInteractionCount":4},"InteractionCounter",{"@type":103},"ViewAction",{"@type":105,"mainEntity":106},"FAQPage",[107,113,117],{"name":108,"@type":109,"acceptedAnswer":110},"Why are apoptosis-related genes important for glioblastoma prognosis in this study?","Question",{"text":111,"@type":112},"Apoptosis is central to tumor development and therapy-related processes, yet the specific prognostic impact of apoptosis-associated genes in GBM is not well defined. The study addresses this gap by building a gene-based prognostic signature.","Answer",{"name":114,"@type":109,"acceptedAnswer":115},"How were the apoptosis-related genes and the prognostic model constructed?",{"text":116,"@type":112},"Transcriptome and single-cell sequencing data from TCGA, CGGA, and GEO were analyzed. Differential apoptosis-related genes were screened with limma, and an apoptosis-related gene prognostic model (AS) was constructed using univariate Cox analysis optimized with combinations of a machine-learning algorithm framework, then validated with bioinformatics tools.",{"name":118,"@type":109,"acceptedAnswer":119},"Which genes were identified as risk or protective factors, and what was the model’s performance?",{"text":120,"@type":112},"BRCA1, CHEK2, and IKBKE showed higher expression in neoplastic tissues and were identified as risk factors, while ZMYND11, MAPK8, and RPS3 were highly expressed in adjacent non-tumor tissues as protective factors. The AS model showed good predictive performance across multiple datasets with higher concordance than conventional outcome indicators.","https://schema.org",{"og:url":83,"og:type":123,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":125,"canonical":83},"index,follow",{"doc_id":127,"site_id":62},438153,1790684432,{"code":4,"msg":5,"data":130},{"doc_id":127,"user_id":131,"nickname":92,"user_avatar":132,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":133,"file_id":134,"file_url":135,"file_type":136,"file_size":137,"view_count":4,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":138,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":128,"read_time":143},3985747859154,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Wiley  \nStem Cells International  \nVolume 2025, Article ID 8727203, 27 pages [https://doi.org/10.1155/sci/8727203](https://doi.org/10.1155/sci/8727203)  \nResearch Article  \nExploration of the Prognostic Role of Apoptosis-Related Genes in Glioblastoma  \nHailong Wang, 1 Lijun Yang, 1 and Yansong Lu 2  \n1Department of Neurosurgery, Jiangshan People ’s Hospital, Quzhou, Zhejiang, China  \n2Department of Neurosurgery, People ’s Hospital of Xinchang, Shaoxing, Zhejiang, China  \nCorrespondence should be addressed to Yansong Lu; [17706856828@163.com](17706856828@163.com)  \nReceived 2 June 2025; Revised 17 August 2025; Accepted 29 September 2025  \nGuest Editor: Sujit Nair  \nCopyright © 2025 Hailong Wang et al. Stem Cells International published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \nBackground: Glioblastoma (GBM) is the most common and aggressive malignant neoplasm in the central nervous system. Apoptosis is crucial in the genesis, progression, and management of tumors. Nevertheless, the influence of apoptosis-associated genes on GBM prognosis is unclear.  \nMethods: Transcriptome data and single-cell sequencing data were obtained from TCGA, CGGA, and GEO databases. Differential genes related to apoptosis were screened using the limma software, and an apoptosis-related gene prognostic model (apoptosis signature [AS] model) was constructed through univariate Cox analysis under the optimization of 101 machine learning algorithm combinations. Validation analyses were conducted using bioinformatics tools.  \nResults: A notable divergence in the expression levels of genes associated with programed cell death was identified when comparing GBM neoplastic tissues to their surrounding non-neoplastic counterparts. They were closely related to the prognosis of GBM patients. BRCA1, CHEK2, and IKBKE genes exhibited elevated levels of expression within neoplastic tissues and were identified as risk factors for prognosis, while ZMYND11, MAPK8, and RPS3 genes were highly expressed in adjacent nontumor tissues as protective factors. The AS model demonstrated good predictive performance across multiple datasets, showing a higher concordance index (C-index) value compared to conventional indicators of outcome. Moreover, the correlation coefficient between HSPB1 and the risk score associated with the AS model was positive, with a value of 0.75 (p \u003C 2.2e–16) .  \nConclusions: An apoptosis-related gene prognostic model (AS model) with high predictive performance was constructed and had close associations with the tumor immune microenvironment and intercellular communication. The HSPB1 had a good predictive effect on GBM prognosis.  \nKeywords: apoptosis-related genes; glioblastoma; intercellular communication; prognostic model; tumor immunity  \n1. Background  \nGlioblastoma (GBM) is a type of neoplasm within the central nervous system and the predominant primary cerebral malignancy, presenting a considerable obstacle in the field ofneurooncology. Despite the current standard treatment described by Roger Stupp et al. [1] 16 years ago, the prognosis remains poor. Despite continuous efforts in basic, translational, and clinical research, long-term survival rates have only changed marginally [2]. The outlook for patients is exceedingly grim, with a median duration of life generally not exceeding 15 months [3]  \nand a 1-year survival probability of merely 41.4%. For those experiencing recurrence, the 1-year survival probability remains at 41.4%, while the 5-year survival probability plummets to 6.8%[4]. Established unfavorable indicators of prognosis encompass advanced age, suboptimal functional status, and incomplete excision. The survival rates were more favorable for younger patients (≤60 years), especially women, while older patients faced poorer outcomes [5] . Supramaximal resecti","cbCaie5izu5ECc1y","https://ap.wps.com/l/cbCaie5izu5ECc1y","pdf",10485048,27,"English","# Background\n## Treatment and prognosis context\n## Apoptosis and apoptosis-related gene rationale\n# Methods\n## Data sources and preprocessing\n## Differential gene screening\n## Construction and validation of the AS prognostic model\n# Results\n## Expression divergence between tumor and non-tumor tissues\n## Risk and protective gene signatures\n## Predictive performance and correlation findings\n# Conclusions\n## AS model significance and HSPB1 prognostic value","[{\"question\":\"Why are apoptosis-related genes important for glioblastoma prognosis in this study?\",\"answer\":\"Apoptosis is central to tumor development and therapy-related processes, yet the specific prognostic impact of apoptosis-associated genes in GBM is not well defined. The study addresses this gap by building a gene-based prognostic signature.\"},{\"question\":\"How were the apoptosis-related genes and the prognostic model constructed?\",\"answer\":\"Transcriptome and single-cell sequencing data from TCGA, CGGA, and GEO were analyzed. Differential apoptosis-related genes were screened with limma, and an apoptosis-related gene prognostic model (AS) was constructed using univariate Cox analysis optimized with combinations of a machine-learning algorithm framework, then validated with bioinformatics tools.\"},{\"question\":\"Which genes were identified as risk or protective factors, and what was the model’s performance?\",\"answer\":\"BRCA1, CHEK2, and IKBKE showed higher expression in neoplastic tissues and were identified as risk factors, while ZMYND11, MAPK8, and RPS3 were highly expressed in adjacent non-tumor tissues as protective factors. The AS model showed good predictive performance across multiple datasets with higher concordance than conventional outcome indicators.\"}]","Exploration of the Prognostic Role of Apoptosis-Related Genes in Glioblastoma | PDF",68]