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. 2023 Feb 13:19:11769343231153293.
doi: 10.1177/11769343231153293. eCollection 2023.

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

Zhengzhong Zhang  1 , Tingting Pang  1 , Min Qi  1 , Gengyun Sun  1
Affiliations

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

Zhengzhong Zhang et al. Evol Bioinform Online. .

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.P-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.

Keywords: Coronavirus disease (COVID-19); RNA network; biological process; ferroptosis; prediction model.

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Conflict of interest statement

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
The study flow chart.
Figure 2.
Figure 2.
The FRDEGs in GSE157103. (A) The volcano plot, red represents the up-regulated genes, blue represents down-regulated genes, and gray represents stable genes in COVID-19 patients compared with Non-COVID-19 patients. (B) Totally 119 FRDEGs in DEGs. (C) The heatmap of the total FRDEGs in two groups. Blue represents low expression and yellow represents high expression in all samples. Abbreviations: DEGs, differently expressed genes; FRDEGs, ferroptosis related differently expressed genes; FRGs, ferroptosis related genes.
Figure 3.
Figure 3.
KEGG and GO enrichment process of FRDEGs. (A) The bubble diagram of GO enrichment analysis. Top 10 of each GO process (BP、MF and CC) are shown in the diagram. (B) The top 10 biological processes of FRDEGs are shown by GO cord, blue represents down-regulated genes and red represents up-regulated genes. (C) Top 20 pathways by KEGG enrichment of FRDEGs are shown by bubble diagram. (D) Sankey diagram of KEGG enrichment analysis and its specific FRDEGs. Abbreviations: BP, biological processing; CC, cellular component; MF, molecular function.
Figure 4.
Figure 4.
The PPI Network of FRDEGs. (A) The interleaved circle diagrams show the protein-related interaction relationships of proteins encoded by FRDEGs. The score of each FRDEGs were calculated according to the MCC algorithm, and the inner circle represents top 30 FRDEGs while the remaining genes to form the outer circle. (B–D) Top 3 clusters of PPI network by ClutsterONE. Yellow represents the top 30 genes by MCC algorithm, green represents the rest genes.
Figure 5.
Figure 5.
Characterization of core genes associated with COVID-19 in FRDEGs. (A) Least absolute shrinkage and selection operator (LASSO) coefficient profiles of COVID-19 related FRDEGs. (B) LASSO regression by 10-fold cross-validation to choose the best tuning parameter. (C) Box plot shows the expression levels of core genes in COVID-19 patients compared with Non-COVID-19 patients. (D) Correlation plot of 7 core genes in all samples. Blue represents positive correlation while red represents negative correlation between the core genes. The magnitude of the correlation coefficient is represented by the numbers in the circles. (E) Box plot shows the scores of constructed core genes between COVID-19 patients and Non-COVID19 patients.
Figure 6.
Figure 6.
Identification of core genes from FRDEGs in COVID-19. (A) Normgram of core genes which are related with the risk of occurrence of COVID-19. (B and C) ROC curves for the predictive evaluation of 7 core genes in the training and validation sets and the area under the respective curves. (D and E) The heatmap of 7 core genes expression in the training set and the validation set.
Figure 7.
Figure 7.
ROC curves of each core genes. (A-G) The ROC curves show the diagnostic value of each core genes in the occurrence of COVID-19.
Figure 8.
Figure 8.
Two clusters of COVID-19 patients and the different clinical syndrome. (A) The unsupervised clustering of COVID-19 patients by core genes and the best rank is k = 2. (B) The prediction scores of core genes between two clusters. (C) Box plot of expression status of core genes in different clusters. (D–F) Differences in clinically important indicators between the two cluster patients.
Figure 9.
Figure 9.
The miRNA-mRNA network. Red rectangle represents the mRNA of 4 differentially expression genes in two clusters, yellow triangle represents the predicted miRNA, green diamond represents miRNA which can bind to different mRNA.
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