{"title":"An Immunogenic Cell Death-Related Gene Signature Predicts the Prognosis and Immune Infiltration of Cervical Cancer.","authors":"Fangfang Sun, Yuanyuan Sun, Hui Tian","doi":"10.1177/11769351251323239","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>Immunogenic cell death (ICD) has been demonstrated to play a critical role in the development and progression of malignant tumors by modulating the anti-tumor immune response. However, its function in cervical cancer (CC) remains largely unexplored. In this study, we aimed to construct an ICD-related gene signature to predict patient prognosis and immune cell infiltration in CC.</p><p><strong>Methods: </strong>The gene expression profiles and clinical data of CC were downloaded from The Cancer Genome Alas (TCGA) and Gene Expression Omnibus (GEO) datasets, serving as the training and testing groups, respectively. An ICD-related gene signature was developed using the LASSO-Cox model. The expression levels of the associated ICD-related genes were evaluated using single-cell data, CC cell lines, and clinical samples in vitro.</p><p><strong>Results: </strong>Two ICD-associated subtypes (cluster 1 and cluster 2) were identified through consensus clustering. Patients classified into cluster 2 demonstrated higher levels of immune cell infiltration and exhibited a more favorable prognosis. Subsequently, an ICD-related gene signature comprising 3 genes (IL1B, IFNG, and FOXP3) was established for CC. Based on the median risk score, patients in both training and testing cohorts were segregated into high-risk and low-risk groups. Further analyses indicated that the estimated risk score functioned as an independent prognostic factor for CC and influenced immune cell abundance within the tumor microenvironment. The up-regulation of the identified ICD-related genes was further validated in CC cell lines and collected clinical samples.</p><p><strong>Conclusion: </strong>In summary, the stratification based on ICD-related genes demonstrated strong efficacy in predicting patient prognosis and immune cell infiltration, which also provides valuable new perspectives for the diagnosis and prognosis of CC.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"24 ","pages":"11769351251323239"},"PeriodicalIF":2.4000,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851768/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/11769351251323239","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"MATHEMATICAL & COMPUTATIONAL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Objectives: Immunogenic cell death (ICD) has been demonstrated to play a critical role in the development and progression of malignant tumors by modulating the anti-tumor immune response. However, its function in cervical cancer (CC) remains largely unexplored. In this study, we aimed to construct an ICD-related gene signature to predict patient prognosis and immune cell infiltration in CC.
Methods: The gene expression profiles and clinical data of CC were downloaded from The Cancer Genome Alas (TCGA) and Gene Expression Omnibus (GEO) datasets, serving as the training and testing groups, respectively. An ICD-related gene signature was developed using the LASSO-Cox model. The expression levels of the associated ICD-related genes were evaluated using single-cell data, CC cell lines, and clinical samples in vitro.
Results: Two ICD-associated subtypes (cluster 1 and cluster 2) were identified through consensus clustering. Patients classified into cluster 2 demonstrated higher levels of immune cell infiltration and exhibited a more favorable prognosis. Subsequently, an ICD-related gene signature comprising 3 genes (IL1B, IFNG, and FOXP3) was established for CC. Based on the median risk score, patients in both training and testing cohorts were segregated into high-risk and low-risk groups. Further analyses indicated that the estimated risk score functioned as an independent prognostic factor for CC and influenced immune cell abundance within the tumor microenvironment. The up-regulation of the identified ICD-related genes was further validated in CC cell lines and collected clinical samples.
Conclusion: In summary, the stratification based on ICD-related genes demonstrated strong efficacy in predicting patient prognosis and immune cell infiltration, which also provides valuable new perspectives for the diagnosis and prognosis of CC.
目的:免疫原性细胞死亡(Immunogenic cell death, ICD)已被证明通过调节抗肿瘤免疫反应在恶性肿瘤的发生和发展中发挥关键作用。然而,其在宫颈癌(CC)中的作用仍未得到充分研究。本研究旨在构建icd相关基因标记,预测CC患者预后和免疫细胞浸润。方法:从The Cancer Genome Alas (TCGA)和gene expression Omnibus (GEO)数据集中下载CC的基因表达谱和临床数据,分别作为训练组和测试组。使用LASSO-Cox模型建立icd相关基因标记。使用单细胞数据、CC细胞系和体外临床样本评估相关icd相关基因的表达水平。结果:通过一致聚类确定了两个icd相关亚型(集群1和集群2)。第2类患者免疫细胞浸润水平较高,预后较好。随后,建立由3个基因(IL1B、IFNG和FOXP3)组成的icd相关CC基因签名,根据中位风险评分将训练组和测试组患者分为高危组和低危组。进一步的分析表明,估计的风险评分是CC的独立预后因素,并影响肿瘤微环境中的免疫细胞丰度。在CC细胞系和收集的临床样本中进一步验证了所鉴定的icd相关基因的上调。结论:综上所述,基于icd相关基因的分层在预测患者预后和免疫细胞浸润方面具有较强的疗效,也为CC的诊断和预后提供了有价值的新视角。
期刊介绍:
The field of cancer research relies on advances in many other disciplines, including omics technology, mass spectrometry, radio imaging, computer science, and biostatistics. Cancer Informatics provides open access to peer-reviewed high-quality manuscripts reporting bioinformatics analysis of molecular genetics and/or clinical data pertaining to cancer, emphasizing the use of machine learning, artificial intelligence, statistical algorithms, advanced imaging techniques, data visualization, and high-throughput technologies. As the leading journal dedicated exclusively to the report of the use of computational methods in cancer research and practice, Cancer Informatics leverages methodological improvements in systems biology, genomics, proteomics, metabolomics, and molecular biochemistry into the fields of cancer detection, treatment, classification, risk-prediction, prevention, outcome, and modeling.