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. 2023 Mar 16;20(7):2742–2752. doi: 10.1111/iwj.14151

Mining the potential therapeutic targets for COVID‐19 infection in patients with severe burn injuries via bioinformatics analysis

Xueyao Cai 1, Jing Deng 1, Wenjun Shi 2, Yuchen Cai 2,✉, Zhengzheng Ma 1,✉
PMCID: PMC10410338  PMID: 36924127

Abstract

The Coronavirus Disease‐19 (COVID‐19) pandemic is posing a serious challenge to human health. Burn victims are susceptible to severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) infection leading to delayed recovery and even profound debilitation. Nevertheless, the molecular mechanisms underlying COVID‐19 and severe burn are yet to be elucidated. In our work, the differentially expressed genes (DEGs) were identified from GSE157852 and GSE19743, and the common DEGs between COVID‐19 and severe burn were extracted. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein–protein interactions (PPI), gene coexpression network, and multifactor regulatory network analysis of hub genes were carried out. A total of 44 common DEGs were found between COVID‐19 and severe burn. Functional analyses indicated that the pathways of immune regulation and cytokine response participated collectively in the development of severe burn and progression of COVID‐19. Ten significant hub genes were identified, including MERTK, SIRPA, TLR3, ITGB1, DPP4, PTPRC, LY75, IFIT1, IL4R, and CD2. In addition, the gene coexpression network and regulatory network were constructed containing 42 microRNAs (miRNAs) and 2 transcription factors (TFs). Our study showed the shared pathogenic link between COVID‐19 and severe burn. The identified common genes and pivotal pathways pave a new road for future mechanistic researches in severe burn injuries complicated with COVID‐19.

Keywords: COVID‐19, differentially expressed genes, hub genes, severe burn, therapy

1. INTRODUCTION

The Coronavirus Disease‐19 (COVID‐19) is a global outbreak of coronavirus, an infectious disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). 1 , 2 According to World Health Organization (https://covid19.who.int/), globally, as of February 21, 2023, a total of 757 264 511 cases of COVID‐19 have been confirmed, including 6 850 594 deaths. Although the most common symptoms of COVID‐19 are fever, cough, chest discomfort, fatigue, and diarrhoea, more serious systematic complications can also occur in immunosuppressed patients upon SARS‐CoV‐2 infection. 3 , 4 , 5 Moreover, dysregulated immune activation and inflammatory response is commonly observed in patients with COVID‐19. 6 In this current COVID‐19 pandemic, given the high incidence of burn injuries on a daily basis, it brings to our plastic surgeons' attention whether patients suffering from severe burn complicated with COVID‐19 infection will lead to the aggravation of the traumatic condition of burn injuries as well as deteriorated COVID‐19 outcomes. 7 , 8

The burn injury is recognised as an acute disease associated with many systemic disorders, including acute respiratory distress syndrome (ARDS) and blood coagulation, which happens to be major complications in COVID‐19 patients requiring urgent and efficient treatment. 9 , 10 Recent studies have suggested alarmingly high admission and mortality rates in severe burn patients from the intensive care unit (ICU) during the pandemic of COVID‐19. 11 , 12 As one of the most immunosuppressed patient groups, severe burn victims are highly susceptible to respiratory viral infections. 13 , 14 They lose their principal immune barrier against microorganism and establish a favourable environment for viral infections, which causes delayed recovery and profound debilitation. 15 Uncontrolled inflammatory response in severe cases can lead to multiple organ dysfunction through systemic circulation including renal failure and cerebral inflammation, and even mortality in worst‐case scenarios. 16 Nevertheless, little is known about the intricate mechanisms involved in COVID‐19 patients sustaining severe burn injuries. A better understanding of their relationship can bring new insights into the common pathogenesis and assist the development of evidence‐based management strategies in these patients.

In this study, based on bioinformatics approaches, we analysed the potential interaction between severe burn and COVID‐19. We identified their common differentially expressed genes (DEGs), followed by functional enrichment analyses for pathway explorations. We screened for pivotal hub genes and constructed the protein–protein interactions (PPI) and gene coexpression network. In addition, we built a gene regulatory network integrating our hub genes and their corresponding transcription factors (TFs) and microRNAs (miRNAs). The study workflow was illustrated in Figure 1.

FIGURE 1.

FIGURE 1

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Schematic illustration of the present study.

2. METHODS

2.1. Microarray data

All transcriptome data were acquired from the database Gene Expression Omnibus (GEO, https://www.ncbi.nlm.nih.gov/geo/). The COVID‐19 dataset GSE157852 contained SARS‐CoV‐2 infected human induced pluripotent stem cell (hiPSC)‐derived monolayer brain cells and brain organoids, with mock controls. 17 The GSE19743 included blood samples from 57 patients with severe burn injuries in early versus middle stage. 18 Gene count data were normalised using the “edgeR” package.

2.2. Characterisation of common DEGs in COVID‐19 and severe burn

We acquired the common DEGs for the datasets GSE157852 and GSE19743 on criteria of P < 0.05. For DEGs visualisation, we plotted the volcanos by “volcano3D” R package and draw the Venn diagram using Jvenn, a plug‐in for the jQuery Javascript library. 19 Boxplots were created to illustrate the differential expression of hub genes within the two datasets.

2.3. Functional enrichment analysis

Gene ontology (GO) was carried out using the “clusterProfiler R" package, including biological processes (BP), cellular components (CC), and molecular functions (MF). Kyoto encyclopaedia of genes and genomes (KEGG) enrichment was accomplished using the database KOBAS (http://kobas.cbi.pku.edu.cn/genelist/). 20 GO and KEGG terms with P < 0.05 and count ≥2 were considered statistically significant.

2.4. Analysis of PPI network and identification of hub genes

To build the PPI network, we uploaded the DEGs to the STRING database (http://string-db.org/) for computational prediction of potential protein interactions. 21 The threshold was set at combined scores ≥0.4 for the DEGs to build the PPI network. We imported the interactions into Cytoscape (version 3.9.0) for visual representation. 22 By using a Cytoscape plug‐in CytoHubba, we constructed seven algorithms to identify the final hub genes, including Closeness, Maximal Clique Centrality (MCC), Degree, Maximum Neighbourhood Component (MNC), Radiality, Stress, and Edge Percolated Component (EPC). For the visualisation of coexpression network of hub genes, we uploaded our data to the online database GeneMANIA (http://genemania.org), which can analyse gene lists and predict gene–gene interactions. 23

2.5. Construction of TFs‐miRNAs‐mRNAs regulatory network

Based on our identified hub genes (mRNAs), we screened for the common miRNAs using the information in online databases including miRTarBase (https://mirtarbase.cuhk.edu.cn), starBase (https://starbase.sysu.edu.cn/starbase2/index.php), and TargetScan (https://www.targetscan.org/vert_72/), as well as TFs in Enrichr (https://maayanlab.cloud/Enrichr/). 24 , 25 , 26 , 27 The expressional results were incorporated into the Cytoscape software for the visualisation of the TFs‐miRNAs‐mRNAs regulatory network.

2.6. Statistical analysis

All results were processed through R software (version 4.1.2). Benjamini‐Hochberg method was applied for adjusted p value calculation. Statistical significance between the two groups was analysed by the two‐sample Wilcoxon test and t test. P‐values were deemed statistically significant when <0.05 (*P < 0.05, **P < 0.01, ***P < 0.001).

3. RESULTS

3.1. Identification of the common DEGs between COVID‐19 and severe burn

A total of 1191 DEGs were extracted from the COVID‐19 dataset GSE157852, including 613 upregulated and 578 downregulated genes in SARS‐CoV‐2 infected hiPSC‐derived monolayer brain cells versus mock controls (Figure 2A). The dataset GSE19743 was designated for the identification of DEGs in severe burn injuries, of which 241 genes were upregulated and 672 genes were downregulated (Figure 2B). As illustrated in Figure 2C, a total of 44 DEGs were identified as commonly expressed through comparative analysis, accounting for 2.14% of the total 2060 DEGs (Table S1).

FIGURE 2.

FIGURE 2

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Identification of common differentially expressed genes (DEGs) between COVID‐19 and severe burn. (A) The volcano plot showing 1191 DEGs in the COVID‐19 dataset GSE157852. (B) The volcano plot revealing 913 DEGs in the severe burn dataset GSE19743. (C) The Venn diagram identified 44 common DEGs in the two datasets, accounting for 2.14% of the total 2060 DEGs.

3.2. Functional enrichment analysis

To highlight the biological features and molecular pathways underlying COVID‐19 and severe burn, we performed the GO and KEGG enrichment analyses of the 44 common DEGs (Figures 3 and 4). In GO analysis, among BP, the common DEGs were primarily involved in immune‐related responses, including regulation of the immune effector process, regulation of T cell activation, heterotypic cell–cell adhesion, leukocyte cell–cell adhesion, and lymphocyte differentiation (Figure 3A, Table S2). The CC of DEGs were mainly enriched in the invadopodium, external side of plasma membrane, membrane raft, membrane microdomain, and anchored component of membrane (Figure 3B, Table S3). For MF, we found these DEGs were active participants in protein tyrosine kinase binding, chemorepellent activity, cell adhesion mediator activity, virus receptor activity, and exogenous protein binding (Figure 3C, Table S4). Enrichment within KEGG implied that the common DEGs were mainly functioned in the following pathways: cytokine‐cytokine receptor interaction, cell adhesion molecules (CAMs), PI3K‐Akt signalling pathway, Influenza A, Hepatitis C, haematopoietic cell lineage, Osteoclast differentiation, cell cycle, toxoplasmosis, Th17 cell differentiation, TNF signalling pathway, and Th1 and Th2 cell differentiation (Figure 4, Table S5). Altogether, the functional analyses demonstrated that pathways of immune regulation and cytokine response participated collectively in the development of a severe burn and progression of COVID‐19.

FIGURE 3.

FIGURE 3

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Gene Ontology (GO) analysis of the common differentially expressed genes (DEGs) between COVID‐19 and severe burn, including (A) biological process (BP), (B) cellular component (CC), and (C) molecular function (MF).

FIGURE 4.

FIGURE 4

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Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis of the common differentially expressed genes (DEGs) between COVID‐19 and severe burn.

3.3. Analysis of hub genes and PPI network

The PPI network was established based on the common DEGs using the STRING database and visualised in Cytoscape. On criteria of combined scores ≥0.4, we obtained a key gene module which included CD84, PTPRC, MERTK, AIM2, ALOX5AP, CDC6, SERPINB2, DPP4, TLR3, NT5E, BUB1B, ADAM28, ITGB1, SIRPA, LY75, IL4R, IFI6, CD2, IFIT1, CXCL6, and SOCS3 (Figure 5A). Subsequently, we screened for the common hub genes based on the intersection of seven algorithms, namely Closeness, MCC, Degree, MNC, Radiality, Stress and EPC (Figure 5B). A total of 10 common hub genes were finally extracted, including MERTK, SIRPA, TLR3, ITGB1, DPP4, PTPRC, LY75, IFIT1, IL4R, and CD2 (Figure 5C). For the validation of hub genes, we compared their differential expression between groups in the two datasets. As shown in Figure 6A, we found a significantly downregulated IFIT1 and upregulated MERTK expression level in middle versus early stage of severe burn. In the COVID‐19 dataset, six significantly less expressed hub genes (PTPRC, DPP4, TLR3, LY75, MERTK, and IFIT1) and two over expressed genes (ITGB1 and IL4R) were observed in COVID‐19 patients versus control subjects (Figure 6B). For building a gene–gene interaction network, we uploaded these hub genes to the GeneMANIA database to decipher their correlations among coexpression, physical interactions, prediction, and genetic interactions (Figure 7). Twenty predicted genes correlated with the 10 common hub genes were identified. The results implied that these genes were strongly associated with positive regulation of leukocyte activation, leukocyte cell–cell adhesion, regulation of T cell activation, positive regulation of leukocyte cell–cell adhesion, regulation of inflammatory response, response to virus, and ERK1 and ERK2 cascade.

FIGURE 5.

FIGURE 5

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Analysis of the common hub genes among COVID‐19 and severe burn. (A) The key gene module showing 21 shared differentially expressed genes (DEGs). (B) The intersection of seven algorithms determined a total of 10 overlapping hub genes. (C) Protein–protein interaction (PPI) network of the 10 hub genes drawn by the Cytoscape plug‐in CytoHubba.

FIGURE 6.

FIGURE 6

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Differential expression of the 10 hub genes in (A) severe burn and (B) COVID‐19 datasets. The vertical coordinates indicate the relative gene expression. ns: no significance, *P < 0.05; **P < 0.01; ***P < 0.001.

FIGURE 7.

FIGURE 7

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Hub genes and their coexpression gene interaction network analysed by GeneMANIA.

3.4. TFs‐miRNAs‐mRNAs regulatory network

For all living cells, TFs and miRNAs are essential regulators controlling gene expression at transcriptional and post‐transcriptional level. 28 Using the online databases, we analysed the TFs and miRNAs in potential interactions with the hub genes. As illustrated in Figure 8, the TFs‐miRNAs‐mRNAs regulatory network contained 42 miRNAs and 2 TFs (IRF9, FOXF1) that can be the promising biomarkers.

FIGURE 8.

FIGURE 8

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Predicted gene regulatory network integrating our hub genes (mRNAs), microRNAs (miRNAs), and transcription factors (TFs). The orange triangles stand for miRNAs, blue rectangles for TFs, and green hexagons for hub genes.

4. DISCUSSION

The emergence of the human coronavirus strain SARS‐CoV‐2 has thrown the world into the midst of a new pandemic, which remains an ongoing threat to human health. 29 Despite the fact that the majority of patients with SARS‐CoV‐2 infections undergo a relatively mild and benign clinical course, recent evidence suggested that COVID‐19 patients sustaining severe burn injuries may experience an amplified inflammatory response leading to delayed recovery and even serious debilitation. 14 , 30 Nevertheless, the common pathophysiological link between COVID‐19 and severe burn remains vastly unclear. In the present study, we tried to dissect their shared pathogenic mechanism through in silico bioinformatics analysis. Based on the 44 common DEGs, functional analyses indicated the potential participation of immune regulatory pathways and cytokine response. Through PPI construction, ten hub genes were determined, including MERTK, SIRPA, TLR3, ITGB1, DPP4, PTPRC, LY75, IFIT1, IL4R, and CD2. Subsequent gene coexpression network and TFs‐miRNAs‐mRNAs regulatory network showed 42 miRNAs and 2 TFs (IRF9, FOXF1) as possible therapeutic targets. As far as we are concerned, the present work is the first to clarify the potential interaction between COVID‐19 and severe burn.

Based on systematic bioinformatics approaches, ten hub genes were identified to participate collectively in the development of COVID‐19 and severe burn injuries, including MERTK, SIRPA, TLR3, ITGB1, DPP4, PTPRC, LY75, IFIT1, IL4R, and CD2. Mer receptor tyrosine kinase (MerTK), which is activated in macrophages upon tissue injuries, has been proposed as therapeutic targets for the resolution of inflammation during the wound healing process. 31 A recent study also demonstrated that serum MERTK levels were significantly lower in COVID‐19 versus healthy participants. 32 Signal regulatory protein alpha (SIRPA) is an essential signalling molecule modulating inflammatory responses in macrophages. 33 It can exhibit inhibitive effect on different viral infections including Zika, Ebola, and SARS‐CoV‐2. 34 Toll‐like receptor 3 (TLR3) is a member of the TLR family, which regulates the transcriptional induction of type I interferons (IFNs) and proinflammatory cytokines to build an antiviral host response. 35 By testing the gene expression in peripheral blood, researchers have found that TLR3 was associated with clinical severity in COVID‐19 patients. 36 In diabetic skin wounds, activation of TLR3 can induce tissue destruction by triggering the production of proinflammatory cytokines and reactive oxygen species. 37 The membrane protein integrin B1 (ITGB1) is found to facilitate the infection of multiple viruses through endocytosis. 38 ITGB1 can mediate the entry of SARS‐CoV‐2, which has been proposed as a possible therapeutic target for the clinical intervention of COVID‐19. 39 Dipeptidyl peptidase 4 (DPP4), or CD26, is a multifunctional, transmembrane glycoprotein involved in various cellular activities including cell adhesion, apoptosis, T cell activation, and immune regulation. In wound healing, DPP4 is functionally involved in immune modulation and fibroblast regulation. 40 Recent researches have also illustrated that DPP4 can modulate the aggressive impact of COVID‐19 on tissues and organs. 41 The leucocyte common antigen, protein tyrosine phosphatase receptor type C (PTPRC), or CD45, is an important player in antigen receptor‐mediated activation of T cell and B cell. 42 In a clinical comparative study, PTPRC was demonstrated as a reliable tool in differentiating severe and non‐severe COVID‐19 cases in the early infection stage. 43 Lymphocyte antigen 75 (LY75) encodes the endocytic receptor DEC‐205, a member of the macrophage mannose receptor family C‐type lectins which is indispensable in T cell homeostasis. 44 , 45 IFN‐induced protein with tetratricopeptide repeats (IFIT) genes belong to the IFN‐stimulated gene family. 46 IFIT1 has been discovered to be upregulated in SARS‐CoV‐2 infected cells, indicating its potential role as a drug target for COVID‐19. 47 Besides, in severe burn victims, IFIT1 expression was significantly increased after burns in a time‐dependent manner. 48 Interleukin‐4 receptor (IL4R) have been well defined as a regulator of allergic inflammation and anti‐viral immunity. 49 , 50 CD2 is a transmembrane glycoprotein of the immunoglobulin superfamily expressed on the surface of immune cells. 51 Recent studies have indicated CD2 as an active biomarker in severe COVID‐19 infection. 52 , 53 Taken together, our findings showed the promising role of these hub genes as biomarkers for the targeting of COVID‐19 infection complicated with severe burn injuries. Their specific pathogenic mechanisms deserve further research efforts.

TFs and miRNAs, which are core regulators of gene expression, has been identified as key molecules for the diagnosis and therapy of various diseases. 54 In the present study, based on the 10 common hub genes, we constructed a TFs‐miRNAs‐mRNAs regulatory network containing 42 miRNAs and 2 TFs (IRF9 and FOXF1). Interferon regulatory factor 9 (IRF9) is an integral TF that mediates the type I IFN antiviral response. 55 Patients of IRF9 deficiencies are inherited with disrupted cellular responses to both type I and III IFNs, which protect them from severe viral illnesses. During the COVID‐19 pandemic, it is reported that an unvaccinated child with inborn error of IRF9 experienced a life‐threatening pneumonia upon infection of SARS‐CoV‐2. 56 In different inflammatory skin models, IRF9 were recognised as essential transcriptional regulators of cellular inflammation. 57 , 58 FOXF1, a forkhead‐box (FOX) family protein, has been indicated as a major immunological mediator, implicated in T cell regulation, autoimmune response, and systemic inflammation. 59 Notably, activation of one FOX family TF, FOXO, showed the possible efficacy to alleviate the inflammatory burst following SARS‐CoV‐2 infection. 60 In the context of the present pandemic, the crosstalk between the hub genes and these gene regulators in the progression of severe burn and COVID‐19 represents a promising outlook for therapy and needs to be further investigated.

Certain limitations should be noted in this work. First, our transcriptome data were obtained from a single COVID‐19 and severe burn dataset, which require external validation for a verification of the results. Second, there is a lack of clinical patient data to prove the reliability of our bioinformatics calculations. In addition, the function of hub genes needs to be further clarified in experimental in vitro models, which will be the emphasis of our future explorations.

5. CONCLUSIONS

To summarise, our study showed the shared pathogenic link between COVID‐19 and severe burn. The identified pivotal genes (MERTK, SIRPA, TLR3, ITGB1, DPP4, PTPRC, LY75, IFIT1, IL4R, and CD2) and common immune‐related pathways provide novel insight into the pathophysiological mechanisms of severe burn injuries complicated with COVID‐19 infections. Further experimental and clinical investigations are warranted to facilitate novel therapeutic strategies for the disease transformation.

AUTHOR CONTRIBUTIONS

Xueyao Cai and Jing Deng: data collection and interpretation, and manuscript writing. Wenjun Shi and Yuchen Cai: manuscript revision. Yuchen Cai and Zhengzheng Ma: study design and supervision.

FUNDING INFORMATION

None declared.

CONFLICT OF INTEREST STATEMENT

The authors declare that there is no conflict of interest.

Supporting information

Table S1. Common DEGs between COVID‐19 and severe burn.

Table S2. BP of GO analysis of the common DEGs.

Table S3. CC of GO analysis of the common DEGs.

Table S4. MF of GO analysis of the common DEGs.

Table S5. KEGG analysis of the common DEGs.

Click here for additional data file. (22.8KB, docx)

ACKNOWLEDGEMENTS

We want to sincerely thank the GEO database for providing the transcriptome datasets.

Cai X, Deng J, Shi W, Cai Y, Ma Z. Mining the potential therapeutic targets for COVID‐19 infection in patients with severe burn injuries via bioinformatics analysis. Int Wound J. 2023;20(7):2742‐2752. doi: 10.1111/iwj.14151

Xueyao Cai and Jing Deng contributed equally to this study.

Contributor Information

Yuchen Cai, Email: cyc0419@126.com.

Zhengzheng Ma, Email: zzhengma@126.com.

DATA AVAILABILITY STATEMENT

All available data can be requested from the corresponding authors.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Table S1. Common DEGs between COVID‐19 and severe burn.

Table S2. BP of GO analysis of the common DEGs.

Table S3. CC of GO analysis of the common DEGs.

Table S4. MF of GO analysis of the common DEGs.

Table S5. KEGG analysis of the common DEGs.

Click here for additional data file. (22.8KB, docx)

Data Availability Statement

All available data can be requested from the corresponding authors.

Articles from International Wound Journal are provided here courtesy of Wiley

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