A novel machine learning-driven immunogenic cell death signature for predicting ovarian cancer prognosis.

IF 1.5 4区 医学 Q4 ONCOLOGY
Translational cancer research Pub Date : 2025-02-28 Epub Date: 2025-02-26 DOI:10.21037/tcr-2025-118
Yali Wang, Peng Zhao, Xude Sun, Felipe Batalini, Gabriel Levin, Hooman Soleymani Majd, Hao Chen, Tingting Gao
{"title":"A novel machine learning-driven immunogenic cell death signature for predicting ovarian cancer prognosis.","authors":"Yali Wang, Peng Zhao, Xude Sun, Felipe Batalini, Gabriel Levin, Hooman Soleymani Majd, Hao Chen, Tingting Gao","doi":"10.21037/tcr-2025-118","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>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 signature and producing certain guiding value for the clinical treatment of OC.</p><p><strong>Methods: </strong>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 factors in the context of personalized medicine.</p><p><strong>Results: </strong>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 addition, potential medications that target specific subgroups at risk were identified.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":23216,"journal":{"name":"Translational cancer research","volume":"14 2","pages":"1359-1374"},"PeriodicalIF":1.5000,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11912067/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Translational cancer research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.21037/tcr-2025-118","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/2/26 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Background: 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 signature and producing certain guiding value for the clinical treatment of OC.

Methods: 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 factors in the context of personalized medicine.

Results: 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 addition, potential medications that target specific subgroups at risk were identified.

Conclusions: 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.

用于预测卵巢癌预后的新型机器学习驱动免疫细胞死亡特征。
背景:卵巢癌(OC)是女性最致命的恶性肿瘤之一,主要是由于缺乏可靠的预测性生物标志物和有效的治疗方法。免疫原性细胞死亡(ICD)在肿瘤中的复杂作用仍然知之甚少,尽管它对增强肿瘤免疫应答具有重要意义。我们致力于开发和验证一种新的icd相关基因标记,为临床治疗OC提供一定的指导价值。方法:采用单样本基因集富集分析(ssGSEA)和加权基因共表达网络分析(WGCNA)对癌症基因组图谱(TCGA)-卵巢癌数据集进行icd相关基因鉴定。使用10种不同的机器学习方法组合构建icd相关签名(ICDRS),然后跨多个数据集进行验证。该模型的预测能力被整合到临床图中,以预测患者的预后。最后,我们评估了各种风险亚组对筛选药物的反应,这些药物旨在解决个性化医疗背景下的特定风险因素。结果:我们确定了72个与ICD相关的预后基因。使用101组合机器学习计算结构开发了一致的ICDRS,在预后和临床应用方面显示出出色的预测准确性。在生物过程、突变谱和肿瘤微环境中的免疫细胞渗透方面,低ICDRS患者与高ICDRS患者存在差异。此外,还确定了针对特定危险亚群的潜在药物。结论:ICDRS在预测OC患者预后方面取得了重大进展,促进了精确预测和个性化治疗途径的探索。需要前瞻性临床试验来验证其临床实用性,并将该模型扩展到其他癌症类型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
2.10
自引率
0.00%
发文量
252
期刊介绍: Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信