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颅内动脉瘤中与铁死亡相关的基因、分子亚型和免疫特征的新见解。

Novel insight into ferroptosis-related genes, molecular subtypes, and immune characteristics in intracranial aneurysms.

机构信息

Department of Neurosurgery, Xiangya Hospital, Central South University, 87 Xiangya Street, Changsha, 410008, Hunan, China.

Departments of Neurosurgery and Cellular and Molecular Physiology, Yale University School of Medicine, New Haven, CT, 06520-8082, USA.

出版信息

Inflamm Res. 2022 Nov;71(10-11):1347-1364. doi: 10.1007/s00011-022-01633-8. Epub 2022 Sep 4.

Abstract

OBJECTIVES

This study aimed to identify the role of ferroptosis in intracranial aneurysm (IA).

METHODS

GSE122897, GSE75436, GSE15629, and GSE75434 datasets were downloaded from the Gene Expression Omnibus database. The differentially expressed ferroptosis-related genes (DEFRGs) were selected to construct a diagnostic model integrating with machine learning. Then, a consensus clustering algorithm was performed to classify IA patients into distinct ferroptosis-related clusters. Functional analyses, including GO, KEGG, GSVA, and GSEA analyses, were conducted to elucidate the underlying mechanisms. ssGSEA and xCell algorithms were performed to uncover the immune characteristics.

RESULTS

We identified 28 DEFRGs between IAs and controls from the GSE122897 dataset. GO and KEGG results showed that these genes were enriched in cytokine activity, ferroptosis, and the IL-17 signaling pathway. Immune analysis showed that the IAs had higher levels of immune infiltration. A four FRGs model (MT3, CDKN1A, ZEP69B, and ABCC1) was established and validated with great IA diagnostic ability. We divided the IA samples into two clusters and found that cluster 2 had a higher proportion of rupture and immune infiltration. We identified 10 ferroptosis phenotypes-related markers in IAs.

CONCLUSION

Ferroptosis and the immune microenvironment are closely associated with IAs, providing a basis for understanding the IA development.

摘要

目的

本研究旨在探讨铁死亡在颅内动脉瘤(IA)中的作用。

方法

从基因表达综合数据库中下载 GSE122897、GSE75436、GSE15629 和 GSE75434 数据集。选择差异表达的铁死亡相关基因(DEFRGs),构建机器学习整合的诊断模型。然后,采用共识聚类算法将 IA 患者分为不同的铁死亡相关聚类。进行功能分析,包括 GO、KEGG、GSVA 和 GSEA 分析,以阐明潜在的机制。采用 ssGSEA 和 xCell 算法揭示免疫特征。

结果

我们从 GSE122897 数据集中鉴定出 28 个 IA 与对照组之间的 DEFRGs。GO 和 KEGG 结果表明,这些基因富集于细胞因子活性、铁死亡和 IL-17 信号通路。免疫分析表明 IA 中存在更高水平的免疫浸润。建立并验证了一个由 MT3、CDKN1A、ZEP69B 和 ABCC1 组成的四个 FRGs 模型,具有良好的 IA 诊断能力。我们将 IA 样本分为两个聚类,发现聚类 2 具有更高的破裂和免疫浸润比例。我们在 IA 中鉴定出 10 个与铁死亡表型相关的标志物。

结论

铁死亡和免疫微环境与 IA 密切相关,为理解 IA 的发生提供了依据。

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