A Bioinformatic Approach Based on Systems Biology to Determine the Effects of SARS-CoV-2 Infection in Patients with Hypertrophic Cardiomyopathy.

Recently, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of coronavirus disease 2019 (COVID-19), has infected millions of individuals worldwide. While COVID-19 generally affects the lungs, it also damages other organs, including those of the cardiovascular system. Hypertrophic cardiomyopathy (HCM) is a common genetic cardiovascular disorder. Studies have shown that HCM patients with COVID-19 have a higher mortality rate; however, the reason for this phenomenon is not yet elucidated. Herein, we conducted transcriptomic analyses to identify shared biomarkers between HCM and COVID-19 to bridge this knowledge gap. Differentially expressed genes (DEGs) were obtained using the Gene Expression Omnibus ribonucleic acid (RNA) sequencing datasets, GSE147507 and GSE89714, to identify shared pathways and potential drug candidates. We discovered 30 DEGs that were common between these two datasets. Using a combination of statistical and biological tools, protein-protein interactions were constructed in response to these findings to support hub genes and modules. We discovered that HCM is linked to COVID-19 progression based on a functional analysis under ontology terms. Based on the DEGs identified from the datasets, a coregulatory network of transcription factors, genes, proteins, and microRNAs was also discovered. Lastly, our research suggests that the potential drugs we identified might be helpful for COVID-19 therapy.

1. Introduction

It has been determined that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel member of the Coronaviridae family and the class of Pisoniviricetes, causes mild and severe respiratory diseases in humans [1-4]. Even though SARS-CoV-2 infections primarily affect the respiratory tract, they frequently cause heart injuries in patients with moderate to severe coronavirus disease 2019 (COVID-19), particularly in those with underlying cardiovascular diseases [5-7]. Furthermore, growing evidence demonstrates a link between COVID-19 and increased mortality from heart failure and cardiovascular diseases [8].

Hypertrophic cardiomyopathy (HCM) is one of the most prevalent inherited heart conditions associated with angiotensin-converting enzyme 2 (ACE2) deficiency in patients with heart failure [9, 10]. SARS-CoV-2 binds with ACE2 and accelerates its degradation, thereby decreasing its ability to counteract the activity of the renin-angiotensin system (RAS) protein [11]. Although the present results suggested that ACE2 expression increased with ACE inhibitor treatment in HCM patients' tissues, they were not statistically significant [12]. Therefore, understanding the impact of SARS-CoV-2 infection in patients with HCM and developing therapeutic drugs that could decrease the odds of complications or death are essential. However, current efforts mainly focus on studying stress cardiomyopathies secondary to COVID-19, such as takotsubo cardiomyopathy [13, 14]. To date, no bioinformatic research on the impact of COVID-19 in patients with preexisting HCM at the molecular level has been reported.

Herein, to bridge the knowledge gap, the cooccurrence of HCM and COVID-19 was examined using two datasets, GSE89714 (HCM) and GSE147507 (COVID-19), obtained from the Gene Expression Omnibus (GEO) database. We identified the differentially expressed genes (DEGs) in each dataset and searched for DEGs shared by the two diseases. These common DEGs, designated as the primary experimental genes, were also used to identify various transcriptional regulators. Then, the hub genes were extracted from these common DEGs using the specific algorithm in the Cytoscape programme. Additionally, the hub genes were used to predict potential therapeutic drugs. Overall, we predicted four agents that could be potentially therapeutic for HCM patients with COVID-19.

2. Materials and Methods

2.1. Study Datasets

The National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/geo/) and GEO databases were used to obtain the COVID-19 and HCM ribonucleic acid sequencing (RNA-seq) datasets [15]. The following criteria were used to assess the quality of the eligible datasets: (1) case-control study; (2) high-throughput sequencing for expression profiling; (3) comparable experimental and control or untreated conditions; (4) more than three samples in each group; and (5) complete raw and processed microarray data was available. The high-throughput Illumina NextSeq 500 RNA sequencing platform was used to obtain the transcriptional profiles of lung biopsy samples from patients with COVID-19 for the GSE147507 [16]. RNA-seq data from heart tissue samples of four participants without HCM and five participants with HCM are included in the GSE89714 dataset. The HiSeq 2000 platform was used for the sequencing experiment. The CuffLinks programme was employed to assess gene expression. Table 1 summarises the two datasets.

Table 1

A description of the two datasets with their GEO information.

Disease name	GEO accession	GEO platform	Total DEG count	Upregulated DEG count	Downregulated DEG count
SARS-CoV-2	GSE147507	GPL18573	1781	1390	391
HCM	GSE89714	GPL11154	207	134	73

Abbreviations: GEO: Gene Expression Omnibus; DEGs: differentially expressed genes; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2; HCM: hypertrophic cardiomyopathy.

The cut-off criteria were set at P < 0.05 and ∣logFC | ≥1.0 to identify significant DEGs in each dataset using the DESEq2 R package. Jvenn online software was used to obtain the shared DEGs between GSE147507 and GSE89714 [17]. DEG expression was considered exclusive between the two datasets if statistically significant differences existed across different conditions [18].

2.2. Gene Ontology (GO) and Pathway Enrichment Analyses

Genome enrichment analysis helps determine the chromosome positions associated with various interrelated diseases [19]. We used an online tool, Enrichr (https://maayanlab.cloud/Enrichr/), to determine the possible molecular pathways and mechanisms involving the common DEGs. The shared pathways between HCM and COVID-19 were examined using four databases: BioCarta, WikiPathways, Reactome, and Kyoto Encyclopedia of Genes and Genomes (KEGG). A P value of < 0.05 was used as a standard metric in quantifying the top-ranked pathways.

2.3. Protein-Protein Interaction (PPI) Network Analysis

The interaction of different cellular proteins can indirectly reflect a protein's functions and roles. Understanding PPI networks can therefore shed light on how proteins function across the board in cellular machinery [20-23]. The shared DEGs were uploaded to the STRING database (https://string-db.org/) [21] to illustrate potential protein connections between HCM and COVID-19. The common DEG PPI network was created using a low confidence score of 0.15. The obtained PPI network was viewed using Cytoscape software (v.3.8.0).

2.4. Hub Gene Extraction and Submodule Analysis

Cytohubba, a validated Cytoscape plugin, ranks and extracts central or targeted elements based on numerous network features. Maximal clique centrality is a commonly used algorithm in Cytohubba for analysing networks from various perspectives [24, 25]. The top 10 hub genes in the obtained PPI network were identified using this method. Additionally, we classified the shortest paths between hub genes based on the calculations from Cytohubba.

2.5. Recognition of Transcription Factors (TFs) and MicroRNAs (miRNAs)

A TF is a protein that binds to gene elements and regulates gene expression [26]. Candidate TFs that are topologically connected to mutual DEGs obtained from the JASPAR database were identified using the NetworkAnalyst platform, a popular web tool for the meta-analysis of gene expression data and viewing biological mechanisms, roles, and gene translation (https://www.networkanalyst.ca/) [27]. JASPAR provides open-access profiles of various TFs in six taxonomic groups [28]. In addition, TarBase and miRTarBase were used to analyse miRNA-targeted gene interactions to find miRNAs that potentially influence gene translation [29, 30]. These online tools can be used by researchers to filter high-degree miRNAs and identify the associated biochemical processes and characteristics to generate the most plausible hypothesis.

2.6. Prediction of Candidate Drugs

Predicting protein-drug interactions (PDIs) or identifying candidate drug molecules was a crucial aspect of this study. Enrichr was used to select potential drug molecules based on the identified DEGs in HCM and COVID-19 and the Drug Signatures database (DSigDB). Gene set libraries enabled by Enrichr allow users to study gene set enrichment at the genome-wide level [31]. Targeted drug substances connected to DEGs were identified using the DSigDB (https://maayanlab.cloud/Enrichr/) [32].

2.7. Gene and Disease Association Analysis

The DisGeNET database links various biomedical aspects of medical conditions with gene-disease relations. It focuses on our growing understanding of human genetic disorders (https://www.networkanalyst.ca/) [33]. We used this tool to determine various diseases related to the common DEGs and their chronic complications.

3. Results

3.1. Identification of DEGs and Common DEGs

Patients with COVID-19 exhibited a differential expression of 1,781 genes, including 1,390 upregulated and 391 downregulated genes after disease exposure. Similarly, various statistical analysis techniques were used to rank the DEGs identified for HCM. All DEGs were identified using a criterion of P < 0.05 and ∣logFC | ≥1. Using the Jvenn online platform, 30 common DEGs were identified between the two datasets (Figure 1). There was a close relationship between the two diseases as they shared several genes [34].

Figure 1

Ribonucleic acid sequencing datasets for hypertrophic cardiomyopathy (HCM) (GSE89714) and coronavirus disease 2019 (COVID-19) (GSE147507) were used in this study. The integrated analysis identified 30 differentially expressed genes shared between COVID-19 and HCM.

3.2. GO and Pathway Enrichment Analyses

Using Enrichr, GO and pathway enrichment analyses were performed. Table 2 summarises the top 10 GO terms in the biological processes, molecular functions, and cellular component categories. DEGs are listed in increasing order based on P value. Figure 2 summarises the linear comparison of the overall ontological analysis of each category. An organism's active pathways reveal how it responds to its inherent modifications. It illustrates the interaction between diseases through basic molecular processes [35]. We examined four global databases, KEGG, WikiPathways, Reactome, and BioCarta, to determine the most important pathways involving the DEGs common to HCM and COVID-19. Table 3 summarises the critical pathways identified based on the examined datasets. Pathway enrichment analysis was performed on the datasets (Figure 3). DEGs are listed in increasing order based on P value. A P value of < 0.05 was used to determine the top functional items and pathways.

Table 2

Gene ontology analysis of common differentially expressed genes between hypertrophic cardiomyopathy and coronavirus disease 2019.

Category	GO ID	Term	P values	Genes
GO biological process	GO:0006939	Smooth muscle contraction	1.10E-06	ACTA2, EDNRA, and MYH11
	GO:0014829	Vascular-associated smooth muscle contraction	6.06E-05	ACTA2, EDNRA
	GO:0048251	Elastic fiber assembly	6.06E-05	MFAP4, MYH11
	GO:0030198	Extracellular matrix organization	7.69E-05	COL1A2, BGN, CYP1B1, TIMP1, and LOXL1
	GO:0046466	Membrane lipid catabolic process	9.71E-05	ENPP2, CYP1B1
	GO:0042310	Vasoconstriction	0.000118625	ACTA2, EDNRA
	GO:0097435	Supramolecular fiber organization	0.000160702	MFAP4, COL1A2, CYP1B1, MYH11, and LOXL1
	GO:0030199	Collagen fibril organization	0.000317018	COL1A2, CYP1B1, and LOXL1
	GO:0085029	Extracellular matrix assembly	0.000588116	MFAP4, MYH11
	GO:0055013	Cardiac muscle cell development	0.000588116	MYH11, MYLK3
GO molecular function	GO:0005105	Type 1 fibroblast growth factor receptor binding	0.007478193	FGF18
	GO:0005111	Type 2 fibroblast growth factor receptor binding	0.007478193	FGF18
	GO:0004528	Phosphodiesterase I activity	0.007478193	ENPP2
	GO:0101020	Estrogen 16-alpha-hydroxylase activity	0.011939153	CYP1B1
	GO:0002020	Protease binding	0.013479908	COL1A2, TIMP1
	GO:0048407	Platelet-derived growth factor binding	0.016380734	COL1A2
	GO:0031432	Titin binding	0.019331061	ANKRD1
	GO:0008191	Metalloendopeptidase inhibitor activity	0.020803015	TIMP1
	GO:0042288	MHC class I protein binding	0.025206075	TUBB4B
	GO:0031690	Adrenergic receptor binding	0.025206075	ARRDC3
GO cellular component	GO:0062023	Collagen-containing extracellular matrix	6.41E-08	MFAP4, COL1A2, ABI3BP, BGN, PLAT, AEBP1, THBS2, and LOXL1
	GO:0031091	Platelet alpha granule	0.000327618	ISLR, TIMP1, and THBS2
	GO:0034774	Secretory granule lumen	0.001211585	C3, ISLR, TIMP1, and TUBB4B
	GO:0005775	Vacuolar lumen	0.001772031	C3, BGN, and TUBB4B
	GO:0031093	Platelet alpha granule lumen	0.004526565	ISLR, TIMP1
	GO:0071953	Elastic fiber	0.007478193	MFAP4
	GO:0035578	Azurophil granule lumen	0.008026254	C3, TUBB4B
	GO:0005788	Endoplasmic reticulum lumen	0.008747528	C3, COL1A2, and TIMP1
	GO:0001527	Microfibril	0.016380734	MFAP4
	GO:0005859	Muscle myosin complex	0.022272833	MYH11

Figure 2

Gene ontology analysis of common differentially expressed genes shared between hypertrophic cardiomyopathy and coronavirus disease 2019 was performed using Enrichr. Terms were evaluated by categories: (a) biological processes, (b) molecular function, and (c) cellular components.

Table 3

Pathway enrichment analysis of common differentially expressed genes between hypertrophic cardiomyopathy and coronavirus disease 2019.

Category	Pathways	P values	Genes
WikiPathways human	IL-18 signaling pathway WP4754	4.84E-05	ACTA2, BTG2, COL1A2, TIMP1, and IER3
	Endochondral ossification with skeletal dysplasias WP4808	0.000119283	FRZB, FGF18, and PLAT
	Endochondral ossification WP474	0.000119283	FRZB, FGF18, and PLAT
	miR-509-3p alteration of YAP1/ECM axis WP3967	0.000291693	EDNRA, THBS2
	miRNA targets in ECM and membrane receptors WP2911	0.000493146	COL1A2, THBS2
	Focal adhesion-PI3K-Akt-mTOR-signaling pathway WP3932	0.00103726	COL1A2, FGF18, THBS2, FGF12
	PI3K-Akt signaling pathway WP4172	0.001585899	COL1A2, FGF18, THBS2, and FGF12
	Focal adhesion WP306	0.003186561	COL1A2, THBS2, and MYLK3
	Complement and coagulation cascades WP558	0.003412595	C3, PLAT
	Genotoxicity pathway WP4286	0.004013249	ACTA2, BTG2
BioCarta	Inhibition of matrix metalloproteinases h reckPathway	0.011939153	TIMP1
	BTG family proteins and cell cycle regulation h btg2Pathway	0.013421829	BTG2
	Alternative complement pathway h alternativePathway	0.014902355	C3
	Lectin induced complement pathway h lectinPathway	0.019331061	C3
	Platelet amyloid precursor protein pathway h plateletAppPathway	0.020803015	PLAT
	Classical complement pathway h classicPathway	0.022272833	C3
	Fibrinolysis pathway h fibrinolysisPathway	0.022272833	PLAT
	Beta-arrestins in GPCR desensitization h bArrestinPathway	0.041187484	EDNRA
	Activation of cAMP-dependent protein kinase, PKA h gsPathway	0.042627715	EDNRA
	Role of Beta-arrestins in the activation and targeting of MAP kinases h barr-mapkPathway	0.044065855	EDNRA
KEGG 2019 human	Vascular smooth muscle contraction	4.48E-05	ACTA2, EDNRA, MYH11, and MYLK3
	Apelin signaling pathway	0.001114898	ACTA2, PLAT, and MYLK3
	Phagosome	0.001503079	C3, THBS2, and TUBB4B
	Focal adhesion	0.003324312	COL1A2, THBS2, and MYLK3
	Regulation of actin cytoskeleton	0.004174068	FGF18, MYH11, and MYLK3
	Calcium signaling pathway	0.005454596	EDNRA, FGF18, and MYLK3
	Complement and coagulation cascades	0.007187738	C3, PLAT
	ECM-receptor interaction	0.007685775	COL1A2;,THBS2
	Platelet activation	0.014809355	COL1A2, MYLK3
	PI3K-Akt signaling pathway	0.015674766	COL1A2, FGF18, and THBS2
Reactome	Extracellular matrix organization R-HSA-1474244	5.84E-05	MFAP4, COL1A2, BGN, TIMP1, and LOXL1
	Smooth muscle contraction R-HSA-445355	0.0011157	ACTA2, MYH11
	Elastic fiber formation R-HSA-1566948	0.001719859	MFAP4, LOXL1
	Signaling by PDGF R-HSA-186797	0.002034436	FGF18, PLAT, THBS2, and IER3
	Assembly of collagen fibrils and other multimeric structures R-HSA-2022090	0.002965286	COL1A2, LOXL1
	Muscle contraction R-HSA-397014	0.003096721	ACTA2, MYH11, and FGF12
	PI5P, PP2A, and IER3 regulate PI3K/AKT signaling R-HSA-6811558	0.006864228	FGF18, IER3
	Collagen formation R-HSA-1474290	0.007187738	COL1A2, LOXL1
	Diseases of glycosylation R-HSA-3781865	0.007685775	BGN, THBS2
	Negative regulation of the PI3K/AKT network R-HSA-199418	0.008026254	FGF18, IER3

Figure 3

Pathway enrichment analysis of the common differentially expressed genes between hypertrophic cardiomyopathy and coronavirus disease 2019 was performed using Enrichr. Different databases were used in the analysis: (a) WikiPathways, (b) BioCarta, (c) Reactome, and (d) Kyoto Encyclopedia of Genes and Genomes 2019 human database.

3.3. Classification of Hub Proteins and Submodules

We predicted the interaction of DEGs by analysing the STRING PPI network using Cytoscape. The PPI network constructed using the common DEGs comprised 30 nodes and 124 edges (Figure 4). Additionally, most of the interconnected nodes in the PPI network were identified as hub genes. Using the Cytohubba plugin, the top 10 DEGs were considered hub genes. This gene list includes thrombospondin 2 (THBS2), biglycan (BGN), collagen type I alpha 2 chain (COL1A2), actin alpha 2 (ACTA2), myosin heavy chain 11 (MYH11), adipocyte enhancer-binding protein 1 (AEBP1), immunoglobulin superfamily containing leucine-rich repeat (ISLR), frizzled-related protein (FRZB), microfibril-associated protein 4 (MFAP4), and lysyl oxidase homolog 1 (LOXL1). These hub genes might be used as biomarkers to identify diseases and develop new therapeutic approaches. To comprehend the connections between the hub genes, we also constructed a submodule network using the Cytohubba plugin (Figure 5).

Figure 4

Protein-protein interaction (PPI) network of common differentially expressed genes (DEGs) between hypertrophic cardiomyopathy and coronavirus disease 2019. The circular nodes represent the DEGs, while the edges represent their interactions. The PPI network has 30 nodes and 124 edges.

Figure 5

Determination of hub genes from the protein-protein interaction network using the latest maximal clique centrality procedure with the Cytohubba plugin in Cytoscape. The nodes highlighted in red or yellow represent the top 10 hub genes and their interactions with other molecules. There are 26 nodes and 119 edges in the network.

3.4. Determination of Regulatory Signatures

There is a network-based approach to identify the transcriptional changes, identify the regulatory TFs and miRNAs, and gain insights into the molecules that regulate hub proteins or common DEGs. Figure 6 illustrates the interactions between the regulatory TFs and DEGs. Figure 7 illustrates the interactions between miRNA regulators and DEGs. According to the analyses of the TF-gene and miRNA-gene interaction networks, 41 TFs and 19 posttranscriptional miRNA signatures regulated more than one DEG, proving that they actively competed with one another.

Figure 6

Differentially expressed gene-transcription factor (TF) regulatory interactions were constructed based on our analyses. The nodes in this diagram represent the TFs, while circular nodes are gene symbols that interact with the TFs.

Figure 7

An interconnected network of differentially expressed genes and microRNAs (miRNAs). The circular node represents miRNAs, while the square nodes represent the interaction between genes and miRNAs.

3.5. Prediction of Candidate Drugs

Understanding the factors responsible for receptor sensitivity requires an assessment of PDIs [36, 37]. We used Enrich to identify four potential drug molecules for HCM and COVID-19 provided by DSigDB. Based on the P value, the top four candidate compounds were extracted. Table 4 lists the most effective drugs identified.

Table 4

The candidate drugs for hypertrophic cardiomyopathy and coronavirus disease 2019.

Name	P value	Chemical formula	Structure
Dasatinib CTD 00004330	2.22E-06	C22H26ClN7O2S
Rapamycin CTD 00007350	2.09E-04	C51H79NO13
Decitabine CTD 00000750	0.001005467	C8H12N4O4
Testosterone enanthate CTD 00000155	0.001963753	C26H40O3

3.6. Determination of Disease Association

Similarities in gene expression between the two conditions can be used to infer disease association and correlation [36, 37]. The first step toward developing therapeutic intervention strategies for diseases is identifying gene-disease relationships [38]. We found that degenerative polyarthritis, hyperkyphosis, and platyspondyly were highly correlated with the hub genes of HCM and COVID-19 (Figure 8). These conditions are complex and multifactorial. Its pathophysiology is influenced by alterations in cell structure, barriers, and environmental factors.

Figure 8

Figure showing the disease-gene association network. Circular nodes represent the gene symbols, and square nodes represent the disease.

4. Discussion

HCM is a common genetic cardiovascular disease that may lead to heart failure. SARS-CoV-2 also infected cardiac cells expressing ACE2, thereby advancing heart failure [39]. Individuals with cardiomyopathy are at high risk of SARS-CoV-2 infection. Herein, we identified molecular targets that could serve as COVID-19 biomarkers. Additionally, these markers might provide crucial details about how they contribute to diseases and conditions. In biomedicine and systems biology research, the expression profiling of high-throughput sequencing data is useful for identifying potential biomarkers [40]. Recently, RNA-seq, a new sequencing method, has significantly improved our ability to examine gene fusions, mutations/single nucleotide polymorphism posttranscriptional modifications, and differential gene expression analyses [41]. As advances in high-throughput sequencing technologies are made, it is becoming more challenging to cope with the increasing bioinformatics data obtained using traditional biological methods. All these limitations may be solved by approaches with artificial intelligence [42].

In this study, our transcriptome analyses revealed that 30 DEGs share similar expression patterns between HCM and COVID-19. GO pathway analysis was performed to obtain insights into the biological significance of the common DEGs in disease progression The smooth muscle contraction pathway and vascular-associated smooth muscle contraction pathway were among the top GO terms identified for the biological process. There is a strong correlation between smooth muscle contraction and SARS-CoV-2 infection, according to several studies. Dysfunction endothelial cells prevent the release of adequate nitrogen oxide (NO), causing smooth muscle constriction [43] and reducing the cells' ability to neutralise reactive oxygen species and release NO [44, 45]. The top two GO pathways identified in the molecular function category are types 1 and 2 fibroblast growth factor (FGF) receptor binders. Cardiac hypertrophy in the postnatal period has been linked to the FGF family, and activating mutations in FGF receptor-1 have been shown to cause HCM [46]. The release of proinflammatory cytokines, such as interleukin- (IL-) 9, IL-10, type 1 FGF, and type 2 FGF, was found in excessive and uncontrolled quantities in critically ill COVID-19 patients [47]. These cytokines are considered valuable biomarkers for evaluating disease progression and potential biological therapeutic targets currently being investigated. In the cellular component category, the top GO terms identified using the common DEGs were collagen-containing extracellular matrix (ECM) and platelet alpha granule. Similarly, the Reactome analysis of the DEGs was mainly enriched in ECM organization (R-HSA-1474244), smooth muscle contraction (R-HSA-445355), and elastic fiber formation (R-HSA-1566948). The ECM comprises fibrillar structures that are made of collagen. Cardiorespiratory disease has been linked to collagen dysfunction [48].

We developed a PPI network based on the identified DEGs to understand how proteins behave biologically and predict potential drug targets. Herein, we used the topological metric (i.e., degree) to identify hub proteins that could serve as COVID-19 potential drug targets or biomarkers and could be linked to various pathological and cellular mechanisms. Most of the top hub proteins identified are associated with HCM and COVID-19 risk factors. These diseases have been linked to ten hub-protein products, including THBS2, BGN, COL1A2, ACTA2, MYH11, AEBP1, ISLR, FRZB, MFAP4, and LOXL1. In this study, a cut-off parameter of 12 degrees was used to identify hub proteins. Cardiorespiratory diseases are significantly impacted by the THBS family of proteins. The effects of circular RNA knockdown on the growth, migration, and necrosis of lung cancer cells are reversed by the overexpression of THBS2, a miR-590-5 target [49]. Additionally, this gene was linked to adenovirus infection [50] and could function as one of COVID-19's possible therapeutic targets. Meanwhile, THBS1 and COL1A1 are genes involved in cardiac remodelling, a hallmark of cardiac hypertrophy [51]. Lastly, BGN ubiquitously exists in the intestinal ECM; thus, BGN could potentially serve as a therapeutic target for HCM patients with COVID-19.

Herein, the TF-gene and miRNA interactions were also analysed to identify potential transcriptional regulators of the common DEGs. TFs and miRNAs regulate gene expression and posttranscriptional RNA silencing, two processes that are crucial to understanding disease development. We discovered connections between the common DEGs, TFs, and miRNAs. The identified TFs, such as the GATA-binding factor 2, histone H4 TF, TF AP-2 alpha, nuclear factor kappa B subunit 1, BGN, and forkhead box C 1, were found to relate to diffident types of developmental and hereditary diseases. Moreover, most of the miRNAs involved in various cancer types (e.g., hsa-mir-29c-3p, hsa-mir-1-3p, and hsa-mir-128-3p) [52-54] and immunity disorders (e.g., hsa-mir-129-2-3p, hsa-mir-16-5p, hsa-mir-182-5p, hsa-mir-27b-3p, and hsa-mir-124-3p) [55-59], as well as TFs related to the corresponding genes, target major proteins to alter their role in disease progression. For example, hsa-mir-29c-3p, hsa-mir-1-3p, and hsa-mir-129-2-3p have been found to target THBS2 [52, 53, 55]. Four miRNAs that we predicted—hsa-mir-376a-5p, hsa-mir-30a-5p, hsa-mir-23b-3p, and hsa-mir-27a-5p—were found to be associated with various HCM-related genes [60-63]. Many of the miRNAs identified are linked to several cancer types, especially lung cancer.

The DEGs and their relation to various diseases were analysed using a gene-disease analysis. Our findings for COVID-19 revealed the involvement of several diseases, such as lung cancer, cardiovascular diseases, blood disorders, liver ailments, and blood coagulation disorders. According to some reviews, SARS-CoV-2 could exacerbate the pathological process of degenerative osteoarthritis. ACE2 expression, RAS imbalances, inflammation, and dysfunction at the molecular level have been suggested as the causative factors [64]. Based on the aforementioned reports, we speculate that systemic inflammation and ischaemia could aggravate cardiac injury in patients with HCM. Hence, anti-inflammatory therapy is particularly important for patients with COVID-19 and HCM.

Herein, we identified dasatinib, a tyrosine kinase inhibitor used for leukaemia. Previous reports predicted that dasatinib could inhibit the binding of SARS-CoV-2 spike protein to ACE2 [65]. However, dasatinib has not yet been previously reported as a treatment option for patients with HCM. By boosting the activation of the mammalian target of rapamycin complex 2, rapamycin, another drug candidate discovered, may be used to reduce inflammation in patients with heart disease [66]. Meanwhile, another drug, decitabine, could increase neoantigen expression to enhance T cell-mediated toxicity against glioblastoma [67]. Testosterone enanthate replacement therapy is commonly used in patients with low testosterone [68]. Additionally, testosterone administration helps suppress the inflammatory response [69] and modulates the immune response, which would be more significant in female patients. We witnessed the first case of corticosteroid and tocilizumab application in reversing the severely reduced left ventricular systolic function due to myocardial depression caused by COVID-19 [70]. This partially demonstrates the clinical viability of our candidate drugs in patients with HCM and paves the way for future pharmaceutical studies. Although we could identify candidate drugs based on our bioinformatics analyses, the findings are also limited in that no experiments or further analytical validation were performed on the data obtained. These reasons could lead to unreliable and imprecise conclusions. Thus, further experiments or clinical trials are necessary to validate their effectiveness and safety.

5. Conclusions

As the COVID-19 vaccine becomes more widely used, more side effects are being reported [71]. Despite the ongoing development of numerous COVID-19 vaccines, mutant SARS-CoV-2 strains continue to appear. According to this study's bioinformatics analysis, the 10 most important genes that HCM and COVID-19 have in common are THBS2, BGN, COL1A2, ACTA2, MYH11, AEBP1, ISLR, FRZB, MFAP4, and LOXL1. Each of these hub genes is essential for various functional mutation developments. Therefore, we used transcriptomic analysis to identify shared pathways and molecular biomarkers between HCM and COVID-19, which could aid in COVID-19 vaccine development and the discovery of novel therapeutic targets.