|Year : 2021 | Volume
| Issue : 1 | Page : 48-55
Bioinformatics analysis identifies potential biomarkers for the prediction and treatment of myocardial infarction
Yu-Yao Ji, Siang Wei, Ran Xu, Run-Da Wu, Kang Yao, Yun-Zeng Zou
Department of Cardiology, Shanghai Institute of Cardiovascular Disease, Zhongshan Hospital, Fudan University, Shanghai, China
|Date of Submission||22-Dec-2020|
|Date of Acceptance||18-Feb-2021|
|Date of Web Publication||30-Mar-2021|
Department of Cardiology, Shanghai Institute of Cardiovascular Disease, Zhongshan Hospital, Fudan University, Shanghai
Source of Support: None, Conflict of Interest: None
Objectives: The aim of this study was to identify differentially expressed genes (DEGs) related to myocardial infarction (MI), which may serve as research and therapeutic targets. Methods: MI expression profiles were obtained from the Gene Expression Omnibus (GEO) database. DEGs were screened using GEO2R, and DEGs in multiple datasets were identified using Venn diagrams. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery v6.8. A protein-protein interaction (PPI) network was constructed using STRING and Cytoscape 3.7.2. Coexpedia was used for gene coexpression network analysis and functional annotation. Results: We identified 50 DEGs in the four datasets, including 29 with important roles in the PPI network. GO functional enrichment analysis revealed the involvement of DEGs in biological processes such as cytokine activation, peptidase inhibition, and chemokine activation. KEGG analysis revealed enrichment in chemokine signaling and cytokine-cytokine receptor interactions. Gene coexpression network analysis identified nine hub genes involved in the occurrence and development of MI including tissue inhibitor of metalloproteinase 1; CD44 antigen; lysyl oxidase; formyl peptide receptor 2; matrix metallopeptidase 3; formyl peptide receptor 1; serine (or cysteine) peptidase inhibitor, clade E, member 1; prostaglandin-endoperoxide synthase 2; and elastin. Conclusions: The hub genes identified may play important roles in MI-related biological processes and represent potential diagnostic and therapeutic targets. Therefore, this study lays a foundation for further exploration of the molecular mechanisms of MI.
Keywords: Gene expression; Genes; Heart disease; Microarray analysis; Signal transduction
|How to cite this article:|
Ji YY, Wei S, Xu R, Wu RD, Yao K, Zou YZ. Bioinformatics analysis identifies potential biomarkers for the prediction and treatment of myocardial infarction. Cardiol Plus 2021;6:48-55
|How to cite this URL:|
Ji YY, Wei S, Xu R, Wu RD, Yao K, Zou YZ. Bioinformatics analysis identifies potential biomarkers for the prediction and treatment of myocardial infarction. Cardiol Plus [serial online] 2021 [cited 2021 Jun 23];6:48-55. Available from: https://www.cardiologyplus.org/text.asp?2021/6/1/48/312597
| Introduction|| |
Myocardial infarction (MI) is the leading cause of cardiovascular disease-related death worldwide. An estimated 3.5 million people die of cardiovascular disease annually in China, and 2.5 million of these patients experience MI. Despite considerable progress in its diagnosis and treatment over the past few decades, the average 5-year relative survival rate for MI remains only 70%–75%. Currently, MI diagnosis mainly relies on electrocardiography, myocardial marker analysis, coronary angiography, and biosensor-based tests; however, it is difficult to achieve early detection with these methods. Thus, increased understanding of MI pathogenesis and the development of effective methods for early screening and diagnosis are essential to increase patient survival.
The Gene Expression Omnibus (GEO) was founded by the United States National Center for Biotechnology Information in 2000, and it contains high-throughput gene expression data submitted by researchers worldwide presented in a convenient platform. We screened four GEO microarray datasets related to MI for identifying differentially expressed genes (DEGs), following which we systematically analyzed and explored their potential functions and interactions through high-throughput bioinformatics. Our research identifies several genes related to MI, increasing our understanding of its molecular mechanisms and providing potential biomarkers for its diagnosis and prognosis.
| Subjects and Methods|| |
Source of microarray data
Four datasets (GSE107568, GSE18703, GSE46395, and GSE775) containing tissues from both mouse models of MI and control samples were downloaded from GEO (www.ncbi.nlm.nih.gov/geo). In these datasets, MI was induced in male C57BL/6 mice through ligation of the descending branch of the left coronary artery. DEGs were analyzed using GEO2R (www.ncbi.nlm.nih.gov/geo/geo2r). The criteria for significance were a | log2 fold change (FC)| ≥1 and an adjusted P ≤ 0.05.
Sample detection and Venn analysis
We used SangerBox to generate volcano graphs for each dataset, and the Draw Venn Diagram tool to generate Venn diagrams showing the overlap between DEGs in the four datasets. DEGs that overlapped in three or more datasets were included in subsequent analyses.
Gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis
Gene ontology (GO) functional enrichment analysis (including analysis of molecular function [MF], biological process [BP], and cellular component [CC] terms) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis of the DEGs were performed in the Database for Annotation, Visualization, and Integrated Discovery (DAVID) v6.8 (http://david.ncifcrf.gov). P < 0.05 was the inclusion criterion.
Protein–protein interaction network construction and hub gene module analysis
DEGs were imported into STRING (https://string-db.org) to identify their protein-protein interactions (PPIs). Cytoscape 3.7.2 was used for further PPI network construction and visualization. Molecular Complex Detection (MCODE) was used to select important modules and genes in the PPI network and display the expression of top ten genes in a module in a box diagram. The threshold criteria for DEG samples were both an MCODE score and node count >2 and a corrected P < 0.05. The top ten hub genes in the PPI network were identified using the cytoHubba plug-in in Cytoscape and analyzed for coexpression using Coexpedia (http://www.coexpedia.org).
| Results|| |
Identification of differentially expressed genes in myocardial infarction
[Figure 1] shows volcano plots for the four datasets. Upregulated and downregulated genes are marked in red and blue, respectively. The data included 23 MI samples and 17 sham samples. Several DEGs were identified between the MI and control groups (P < 0.05, |log2 FC| ≥1.0), including 36 and 14 genes that were upregulated and downregulated in MI, respectively [Table 1].
|Figure 1: Identification of DEGs.|
A-D, Volcano plot in microarray representing top DEGs in GSE107568, GSE18703, GSE46395, GSE775 between MI and controls tissues. Top DEGs are represented satisfying the criteria of logFC value and adjust P < 0.05. The color indicates high expression (red) and low expression (green). E, Venn diagram intersection of the DEGs of the four datasets.
DEGs: Differentially expressed genes, MI: Myocardial infarction
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|Table 1: Cellular compartments enriched for myocardial infarction-related differentially expressed genes|
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Gene ontology enrichment and Kyoto Encyclopedia of Genes and Genome pathway analyses
To identify the functions of the DEGs, they were divided into upregulated and downregulated groups and analyzed for enrichment in GO terms and KEGG pathways. The upregulated genes were significantly enriched for cytokine activators, peptidase inhibitors, chemokine activators, N-A acylpeptide receptors, complement receptors, serine endopeptidase inhibitors, growth factors, and chemokine receptor-binding proteins; these genes were mainly involved in cytokine-cytokine receptor interaction signaling pathways. The downregulated genes were mainly enriched in the pathways controlling retrograde endocannabinoid, serotoninergic synapse, and oxytocin signal transduction. The 29 genes that met the threshold criteria were used in further enrichment analysis. These DEGs were enriched for the MF terms peptidase inhibitor activity, chemokine activity, cytokine activity, N-formyl peptide receptor activity, RAGE-RAGE binding, serine endopeptidase inhibitor activity, complement receptor activity, growth factor activity, chemokine-chemokine receptor binding, and zinc ion binding function. Their enriched BPs were related to the inflammatory response, including positive regulation of inflammation, cell chemotaxis, negative regulation of cell proliferation, and negative regulation of endopeptidase activity. The DEGs were enriched in the extracellular space, extracellular region, extracellular matrix, protein extracellular matrix, protein complexes, extracellular bodies, and intracellular membrane-bound organelles. KEGG pathway analysis [Table 2] revealed that DEGs were mainly enriched in the chemokine signaling pathways and cytokine-cytokine receptor interaction pathways [Figure 2].
|Table 2: Kyoto encyclopedia of genes and genome pathway and gene ontology enrichment analyses of differentially expressed genes|
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Construction of an myocardial infarction-related protein-protein interaction network and key module selection
To further study the roles of MI-related DEGs, we used PPI data from STRING to construct a network containing 29 nodes and 47 edges [Figure 3]A. We identified a potential key module and generated a coexpression network for it, consisting of ten nodes and 26 edges [Figure 3]B. The proteins in the network mainly function in inflammation, complement receptor signaling, leukocyte migration, phospholipase C-activated G protein-coupled receptor signaling, cellular chemotaxis, and the regulation of cell proliferation.
|Figure 3: Construction of protein-protein interaction network and key module selection.|
A, PPI network. B, Gene module with the highest degree of tight connection.
PPI: Protein–protein interaction.
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Coexpression analysis and functional annotation of hub genes
The top ten hub genes included formyl peptide receptor 2 (Fpr2); formyl peptide receptor 1 (Fpr1); pro-platelet basic protein (Ppbp); CD44 antigen (Cd44); lysyl oxidase (Lox); elastin (Eln), prostaglandin-endoperoxide synthase (Ptgs2); matrix metallopeptidase 3 (Mmp3); tissue inhibitor of metalloproteinase 1 (Timp1); and serine (or cysteine) peptidase inhibitor, clade E, member 1 (Serpine1). All ten genes were upregulated in MI mice compared to sham mice [Figure 4]. In coexpression analysis, all hub genes, except Ppbp, scored >10.
We used Coexpedia website(http://www.coexpedia.org) to analyze the 10 above hub genes. There are 9 genes scored more than10, which are Timp1, Cd44, Lox, Fpr2, Mmp3, Fpr1, Serpine1, Ptgs2, Eln respectively [Figure 5]. TIMP1 performs multiple functions including promoting osteocyte apoptosis, immune responses, endocytosis, and endocytosis-related biological effects. CD44 plays key roles in regulating α-smooth muscle actin expression and promoting hyaluronan-mediated muscle insulin resistance. Lox encodes a precursor protein that belongs to the lysine oxidase family. The secreted proprotein is processed into an active mature enzyme and a propeptide that are essential to suppress tumors by inhibiting RAS protooncogene signaling. The active enzyme plays an important role in not only the crosslinking of collagen and elastin but also the development of the cardiovascular system, respiratory system, skin, and connective tissue. In mice, complete knockout of Fpr2 alleviates high fat diet-induced obesity, insulin resistance, hyperglycemia, hyperlipidemia, and hepatic steatosis. Moreover, the loss of FPR2 raised the body temperature, reduced fat mass, and inhibited inflammation by reducing macrophage infiltration and M1 polarization in metabolic tissues. MMP3 is an extracellular matrix-degrading enzyme involved in tissue remodeling, wound repair, atherosclerosis progression, and tumor invasion. FPR1 activation induces actin polymerization in vascular smooth muscle cells. Its absence in the vascular system significantly reduces vasoconstriction by interrupting actin polymerization, leading to a loss of myogenic tension and increased intraluminal pressure. SERPINE1 promotes MMP activity. PTGS2, a target of many nonsteroidal anti-inflammatory drugs, upregulates the expression of prostaglandin G/H synthase family members during inflammation. Abnormal PTGS2 regulation is related to an increased risk of cardiovascular events as well as cancer progression in multiple tissues. ELN is an extracellular matrix protein that contributes to the structures of various tissues including the lungs and arteries.
| Conclusion|| |
Despite considerable progress over the past few decades, the overall mortality rate of MI remains high. Therefore, understanding its pathogenesis and identifying effective molecular biomarkers are essential to improve the survival of patients with MI. Recently, accumulating evidence has demonstrated that DEGs play important roles in the occurrence and development of MI in humans. In this study, 50 MI-related DEGs were identified in 4 GEO datasets, including 36 upregulated genes and 14 downregulated genes. These DEGs were primarily enriched in chemokine signaling and cytokine–cytokine receptor interaction pathways. We constructed a DEG PPI network and coexpression network of the top ten hub genes. Of these, Timp1, Cd44, Lox, Fpr2, Mmp3, Fpr1, Serpine1, Ptgs2, and Eln were identified as potential targets for molecular MI detection and treatment, based on their biological functions and clinical implications.
Functional pathway enrichment analysis found that the DEGs were closely related to peptidase inhibitor activity, chemokine activity, cytokine activity, N-formyl peptide receptor activity, RAGE-RAGE binding, serine endopeptidase inhibitor activity, complement receptor activity, growth factor activity, chemokine-chemokine receptor binding, and zinc ion binding function. Peptidase inhibitor activity is associated with the occurrence of various diseases.,, Chemokines have various functions, such as regulating intracellular homeostasis, inducing the migration and development of leukocytes, and activating the immune system. The unbalanced expression of chemokines and their receptors can lead to various diseases. A recent clinical study revealed changes in the transcription levels of bone marrow-derived macrophages in patients with MI, which dysregulated chemokine/chemokine receptor expression. In addition, Lin et al. confirmed that serum levels of the chemokine, C-X-C motif chemokine ligand 9, were significantly higher in patients with MI than in healthy individuals. When β-adrenergic receptors are impaired, chemokines specifically upregulate the expression of initial cytokines in the heart, promoting early macrophage infiltration and eventually leading to cardiac inflammation, and this study indicated that cytokine activity might affect MI by regulating cardiac immune function. N-formyl peptide receptors are involved in innate immune regulation and host defense. N-formyl peptide receptor-specific interactions might promote pathological inflammatory responses by affecting the endogenous anti-inflammatory system. RAGE is a cell adhesion molecule that plays important roles in chronic inflammation and atherosclerosis. In combination with high mobility group box 1, RAGE promotes vascular inflammation and endothelial cell apoptosis and mediates vascular injury during ischemia-reperfusion injury. Serine endopeptidase inhibitor activity has also been linked to MI, as studies have shown that Buyang Huanwu Decoction can cure cerebral infarction by regulating serine endonuclease activity. The complement cascade is an important component of the innate immune system and is mainly responsible for clearing pathogens and damaged cells. Platelet complement 3a (C3a) receptor (C3aR) and glycoprotein IIb/IIIa are coexpressed in the thrombi of patients with MI. The platelet C3a/C3aR signaling axis affects MI and stroke by regulating platelet adhesion and proliferation-induced thrombosis. Recent studies revealed that epidermal growth factor could reduce oxidative stress and myocardial ischemia-reperfusion injury by activating nuclear factor erythroid 2-related factor 2. Most studies on C-C motif chemokine receptor (CCR) have been related to oncology; however, a recent study found that CCR9 level significantly increased in failing human hearts and a mouse model of myocardial hypertrophy, indicating that more research should be conducted regarding the influence of CCRs on MI. Zinc ions are involved in regulating the intracellular signaling pathways of innate and adaptive immune cells. The Trial to Assess Chelation Therapy demonstrated a protective effect of high-dose zinc supplementation during chelation therapy in patients with diabetes after MI.
We constructed a PPI network to identify hub genes as well as a coexpression network comprising the top ten hub genes. We then identified nine promising hub genes, including Timp1, Cd44, Lox, Fpr2, Mmp3, Fpr1, Serpine1, Ptgs2, and Eln, which were all upregulated in MI. Timp1 is a risk factor for two-leaf aortic valve and aortic disease in Turner syndrome. Timp1 copy number is positively related to the risk of aortic disease. Mayer et al. confirmed that Timp1 could be an indicator of early myocardial ischemia or infarction. Cd44 is related to atherosclerosis in humans and mice, and it is involved in the occurrence, decomposition, and healing of heart inflammation after MI. LOX is a potential therapeutic target for cancer and pulmonary fibrosis., Clinical studies have shown that Lox-1 levels, sLox-1/membrane-bound Lox-1 ratios, and circulating levels in patients with ST-segment elevation MI were higher than those in patients with non-ST-segment elevation MI. FPR1 and FPR2 play important roles in host immune responses and inflammation-related diseases. In mice, the systemic knockout of Fpr2 alleviated high-fat diet-induced obesity, insulin resistance, hyperglycemia, and liver steatosis. FPR2 expression is significantly increased in MI, and Zhang et al. identified FPR1 as a biomarker of acute MI based on a weighted gene co-expression network analysis. MMP3 is widely recognized as a key target gene of hypoxia-inducible factor-1α, the main regulator of the cellular response to hypoxia. Serum MMP3 levels were significantly higher in patients with acute MI with morbidity compared to those without complications, implying a significant correlation between MMP3 and the risk of morbidity after MI. SERPINE1 regulates angiogenesis and the plasminogen activator system, and it is abundantly expressed in coronary endothelial dysfunction and in infarcted myocardium. Ge et al. found that microRNA-26b could inhibit PTGS2 from activating the mitogen-activated protein kinase pathway, alleviating MI, and improving myocardial remodeling. Elastic arteries are composed of cells and a special extracellular matrix that provides reversible elasticity and strength. ELN is the matrix protein that imparts reversible elasticity, which reduces the load on the heart and suppresses the pulsatile blood flow of the distal artery. LOX controls extracellular matrix formation by cross-linking collagen and elastin chains, and it is involved in heart failure, MI, and myocardial hypertrophy. The aforementioned studies support the potential of these nine hub genes as diagnostic markers and therapeutic targets for MI.
In summary, through bioinformatics analysis, we systematically explored the roles of MI-related DEGs and identified nine hub genes that could be involved in the occurrence and progression of MI and cardiac remodeling. To date, only a few bioinformatics analyses of candidate genes for MI diagnosis and treatment have been conducted. Our results shed light on the pathogenesis of MI and provide new potential therapeutic targets and prognostic markers.
Financial support and sponsorship
This study was financially by The National Natural Science Foundation of China (No.: 81900245 and 81730009).
Conflicts of interest
Yun-Zeng Zou is the Board member of the journal.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4], [Figure 5]
[Table 1], [Table 2]