Identification of immunogenic cell death-related gene classification patterns and immune infiltration characterization in ischemic stroke based on machine learning.
Identification of immunogenic cell death-related gene classification patterns and immune infiltration characterization in ischemic stroke based on machine learning.
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DOI：
10.3389/fncel.2022.1094500
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发表时间：
2022
影响因子：
5.3
通讯作者：
Zhang, Shenqi
中科院分区：
文献类型：
--
作者：
Cai, Jiayang;Ye, Zhang;Hu, Yuanyuan;Yang, Ji'an;Wu, Liquan;Yuan, Fanen;Zhang, Li;Chen, Qianxue;Zhang, Shenqi
缺血性中风（IS）占中风的80%以上，是世界上死亡和残疾的主要原因之一。由于治疗时间窗窄，严重出血的发生率高，早期静脉溶栓药物治疗患者获益较少。因此，迫切需要探索脑卒中后的分子机制，以推动新的治疗方法的发展。免疫原性细胞死亡（ICD）是一种调节性细胞死亡（RCD），足以激活免疫活性宿主的适应性免疫应答。虽然有越来越多的证据表明ICD对免疫应答和免疫应答的调节在IS的发展中起着重要作用，但ICD在IS发病机制中的作用很少被探索。在这项研究中，我们系统地评估了ICD相关基因在IS。对缺血再灌注相关基因的表达谱进行了系统的研究。我们使用ICD差异表达基因对IS样本进行了一致聚类、免疫浸润分析和功能富集分析。结果表明，IS患者可分为两个集群，并在不同集群的免疫浸润模式发生改变。此外，我们还进行了机器学习，以筛选出9个可用于预测疾病发生的特征基因。我们还构建了基于9个风险基因（CASP 1、CASP 8、ENTPD 1、FOXP 3、HSP 90 AA 1、IFNA 1、IL 1 R1、MYD 88和NT 5E）的列线图模型，并探讨了9个风险基因的免疫浸润相关性、基因-miRNA和基因-TF调控网络。本研究为进一步阐明IS的发病机制提供了有价值的参考，并为IS的药物筛选、个体化治疗和免疫治疗提供了指导。
Ischemic stroke (IS) accounts for more than 80% of strokes and is one of the leading causes of death and disability in the world. Due to the narrow time window for treatment and the frequent occurrence of severe bleeding, patients benefit less from early intravenous thrombolytic drug therapy. Therefore, there is an urgent need to explore the molecular mechanisms poststroke to drive the development of new therapeutic approaches. Immunogenic cell death (ICD) is a type of regulatory cell death (RCD) that is sufficient to activate the adaptive immune response of immunocompetent hosts. Although there is growing evidence that ICD regulation of immune responses and immune responses plays an important role in the development of IS, the role of ICD in the pathogenesis of IS has rarely been explored. In this study, we systematically evaluated ICD-related genes in IS. The expression profiles of ICD-related genes in IS and normal control samples were systematically explored. We conducted consensus clustering, immune infiltration analysis, and functional enrichment analysis of IS samples using ICD differentially expressed genes. The results showed that IS patients could be classified into two clusters and that the immune infiltration profile was altered in different clusters. In addition, we performed machine learning to screen nine signature genes that can be used to predict the occurrence of disease. We also constructed nomogram models based on the nine risk genes (CASP1, CASP8, ENTPD1, FOXP3, HSP90AA1, IFNA1, IL1R1, MYD88, and NT5E) and explored the immune infiltration correlation, gene-miRNA, and gene-TF regulatory network of the nine risk genes. Our study may provide a valuable reference for further elucidation of the pathogenesis of IS and provide directions for drug screening, personalized therapy, and immunotherapy for IS.
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