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铁死亡的生物学过程与COVID-19发病机制的关系以及与该疾病发生和严重程度相关的核心铁死亡基因。

The Biological Processes of Ferroptosis Involved in Pathogenesis of COVID-19 and Core Ferroptoic Genes Related With the Occurrence and Severity of This Disease.

作者信息

Zhang Zhengzhong, Pang Tingting, Qi Min, Sun Gengyun

机构信息

Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Anhui Medical University, Hefei, China.

出版信息

Evol Bioinform Online. 2023 Feb 13;19:11769343231153293. doi: 10.1177/11769343231153293. eCollection 2023.

Abstract

BACKGROUND

A worldwide outbreak of coronavirus disease 2019 (COVID-19) has resulted in millions of deaths. Ferroptosis is a form of iron-dependent cell death which is characterized by accumulation of lipid peroxides on cellular membranes, and is related with many physiological and pathophysiological processes of diseases such as cancer, inflammation and infection. However, the role of ferroptosis in COVID-19 has few been studied.

MATERIAL AND METHOD

Based on the RNA-seq data of 100 COVID-19 cases and 26 Non-COVID-19 cases from GSE157103, we identified ferroptosis related differentially expressed genes (FRDEGs, adj.-value < .05) using the "Deseq2" R package. By using the "clusterProfiler" R package, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Next, a protein-protein interaction (PPI) network of FRDEGs was constructed and top 30 hub genes were selected by cytoHubba in Cytoscape. Subsequently, we established a prediction model for COVID-19 by utilizing univariate logistic regression and the least absolute shrinkage and selection operator (LASSO) regression. Based on core FRDEGs, COVID-19 patients were identified as two clusters using the "ConsenesusClusterPlus" R package. Finally, the miRNA-mRNA network was built by Targetscan online database and visualized by Cytoscape software.

RESULTS

A total of 119 FRDEGs were identified and the GO and KEGG enrichment analyses showed the most important biologic processes are oxidative stress response, MAPK and PI3K-AKT signaling pathway. The top 30 hub genes were selected, and finally, 7 core FRDEGs (JUN, MAPK8, VEGFA, CAV1, XBP1, HMOX1, and HSPB1) were found to be associated with the occurrence of COVID-19. Next, the two patterns of COVID-19 patients had constructed and the cluster A patients were likely to be more severe.

CONCLUSION

Our study suggested that ferroptosis was involved in the pathogenesis of COVID-19 disease and the functions of core FRDEGs may become a new research aspect of this disease.

摘要

背景

2019年冠状病毒病(COVID-19)在全球范围内爆发,已导致数百万人死亡。铁死亡是一种铁依赖性细胞死亡形式,其特征是细胞膜上脂质过氧化物的积累,并且与癌症、炎症和感染等疾病的许多生理和病理生理过程相关。然而,铁死亡在COVID-19中的作用鲜有研究。

材料与方法

基于来自GSE157103的100例COVID-19病例和26例非COVID-19病例的RNA测序数据,我们使用“Deseq2”R包鉴定了铁死亡相关差异表达基因(FRDEGs,校正P值<0.05)。通过使用“clusterProfiler”R包,我们进行了基因本体(GO)和京都基因与基因组百科全书(KEGG)通路富集分析。接下来,构建了FRDEGs的蛋白质-蛋白质相互作用(PPI)网络,并通过Cytoscape中的cytoHubba选择了前30个枢纽基因。随后,我们利用单变量逻辑回归和最小绝对收缩和选择算子(LASSO)回归建立了COVID-19的预测模型。基于核心FRDEGs,使用“ConsenesusClusterPlus”R包将COVID-19患者分为两个簇。最后,通过Targetscan在线数据库构建miRNA-mRNA网络,并使用Cytoscape软件进行可视化。

结果

共鉴定出119个FRDEGs,GO和KEGG富集分析表明最重要的生物学过程是氧化应激反应、MAPK和PI3K-AKT信号通路。选择了前30个枢纽基因,最终发现7个核心FRDEGs(JUN、MAPK8、VEGFA、CAV1、XBP1、HMOX1和HSPB1)与COVID-19的发生有关。接下来,构建了COVID-19患者的两种模式,A簇患者可能病情更严重。

结论

我们的研究表明铁死亡参与了COVID-19疾病的发病机制,核心FRDEGs的功能可能成为该疾病的一个新的研究方向。

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