[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125374-en":3,"doc-seo-125374-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125374,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","A Novel Machine Learning-Driven Immunogenic Cell Death Signature for Predicting Ovarian Cancer Prognosis","Ovarian cancer is among the most lethal malignancies in women, largely due to limited predictive biomarkers and insufficiently effective therapies. The immunogenic cell death (ICD) program in ovarian cancer remains incompletely defined, despite its potential to enhance anti-tumor immune responses. This study develops and validates an ICD-related gene signature using machine-learning modeling and multiple dataset validation, integrating results into a prognostic nomogram and supporting risk-stratified therapeutic exploration.","A novel machine learning-driven immunogenic cell death signature for predicting ovarian cancer prognosis  \nYali Wang1, Peng Zhao2, Xude Sun3, Felipe Batalini4, Gabriel Levin5,6, Hooman Soleymani majd7 ^, Hao Chen8, Tingting Gao9  \n1Department of Obstetrics and Gynecology, Maternal and Child Health Center in Fuping County, Fuping, China; 2Oncology Department, Xi’an Daxing Hospital, Xi’an, China; 3Department of Anesthesia, The Second Affiliated Hospital of Air Force Medical University, Xi’an, China; 4Department of Medical Oncology, Mayo Clinic Arizona, Phoenix, AZ, USA; 5Division of Gynecologic Oncology, Jewish General Hospital, McGill University, Montreal, QC, Canada; 6The Department of Gynecologic Oncology, Hadassah Medical Center, Faculty of Medicine, Hebrew University, Jerusalem, Israel; 7Oxford University Hospitals NHS Foundation Trust, Department of Gynaecology Oncology, Churchill Hospital, Oxford, United Kingdom; 8Department of Thoracic Surgery, Tangdu Hospital of Air Force Military Medical University, Xi’an, China; 9Department of Obstetricsand Gynecology, The Second Affiliated Hospital of Air Force Medical University, Xi’an, China  \nContributions: (I) Conception and design: Y Wang, T Gao; (II) Administrative support: Y Wang, T Gao; (III) Provision of study materials or patients: P Zhao, H Chen; (IV) Collection and assembly of data: X Sun, H Chen; (V) Data analysis and interpretation: T Gao; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.  \nCorrespondence to: Tingting Gao, MD. Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Air Force Medical University, 569 Xinsi Road, Xi’an 710000, China. Email: [ttgao1981@126.com](ttgao1981@126.com).  \nBackground: Ovarian cancer (OC) is one of the most lethal malignancies in women, primarily due to the absence of reliable predictive biomarkers and effective therapies. The complex role of immunogenic cell death (ICD) in OC remains poorly understood, despite its critical implications for enhancing immune responses against tumors. We are committed to developing and validating a novel ICD-related gene  \nsignature and producing certain guiding value for the clinical treatment of OC.  \nMethods: We employed single-sample gene set enrichment analysis (ssGSEA) and weighted gene coexpression network analysis (WGCNA) on The Cancer Genome Atlas (TCGA)-ovarian carcinoma dataset to identify ICD-associated genes. A combination of 10 different machine learning approaches was used to construct an ICD-related signature (ICDRS), which was then validated across multiple datasets. The model’s predictive power was integrated into a clinical nomogram to predict patient outcomes. Ultimately, we assessed the reaction of various risk subgroups to screen pharmaceuticals designed to address specific risk  \nfactors in the context of personalized medicine.  \nResults: We identified 72 prognostic genes related to ICD. An unanimous ICDRS was developed using a 101-combination machine learning computational structure, demonstrating outstanding predictive accuracy for prognosis and clinical use. Patients categorized as low ICDRS varied from those of high ICDRS in terms of biological processes, mutation profiles, and immune cell penetration in the tumor microenvironment. In  \naddition, potential medications that target specific subgroups at risk were identified.  \nConclusions: The ICDRS presents a significant advancement for prognostication of patients with OC, facilitating refined predictions and the exploration of personalized treatment pathways. Prospective clinical trials are necessary to validate its clinical utility and expand the application of this model to other cancer types.  \nKeywords: Machine learning; ovarian cancer (OC); tumor microenvironment  \nSubmitted Jan 14, 2025. Accepted for publication Feb 18, 2025. Published online Feb 26, 2025.  \ndoi: 10.21037/tcr-2025-118  \nView this article at: [https://dx.doi.org/10.21037/tcr-2025-118](https://dx.d","cbCaiqWs82o4HMgN","https://ap.wps.com/l/cbCaiqWs82o4HMgN","pdf",11889182,1,16,"English","en",105,"# Introduction\n## Background and clinical need\n## ICD and programmed cell death framework\n# Methods\n## Gene discovery using ssGSEA and WGCNA\n## Signature construction and validation\n## Nomogram and risk stratification\n# Results\n## ICD-related prognostic genes and signature performance\n## Biological and immune microenvironment differences\n## Risk-group targeted medication discovery\n# Conclusions","[{\"question\":\"What problem does the study address in ovarian cancer care?\",\"answer\":\"It addresses the lack of reliable predictive biomarkers and the limited understanding of how immunogenic cell death contributes to ovarian cancer prognosis.\"},{\"question\":\"How is the ICD-related gene signature constructed and validated?\",\"answer\":\"The study uses ssGSEA and weighted gene coexpression network analysis on a TCGA ovarian carcinoma dataset to identify ICD-associated genes, then applies a combination of machine-learning approaches to build an ICD-related signature and validates it across multiple datasets.\"},{\"question\":\"What is the practical output of the signature for clinicians?\",\"answer\":\"The signature is integrated into a clinical nomogram to predict patient outcomes and to support risk-group–based personalized treatment pathway exploration, including identification of potential subgroup-targeted medications.\"}]","A Novel Machine Learning-Driven Immunogenic Cell Death Signature for Predicting Ovarian Cancer Prognosis | 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problem does the study address in ovarian cancer care?","Question",{"text":75,"@type":76},"It addresses the lack of reliable predictive biomarkers and the limited understanding of how immunogenic cell death contributes to ovarian cancer prognosis.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the ICD-related gene signature constructed and validated?",{"text":80,"@type":76},"The study uses ssGSEA and weighted gene coexpression network analysis on a TCGA ovarian carcinoma dataset to identify ICD-associated genes, then applies a combination of machine-learning approaches to build an ICD-related signature and validates it across multiple datasets.",{"name":82,"@type":73,"acceptedAnswer":83},"What is the practical output of the signature for clinicians?",{"text":84,"@type":76},"The signature is integrated into a clinical nomogram to predict patient outcomes and to support risk-group–based personalized treatment pathway exploration, including identification of potential 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