In silico identification of common and specific signatures in coronary heart diseases
- Authors:
- Published online on: August 13, 2020 https://doi.org/10.3892/etm.2020.9121
- Pages: 3595-3614
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Copyright: © Yang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
Abstract
Introduction
Coronary heart disease (CHD), also known as coronary artery disease (CAD) is one of a group of diseases of the heart blood vessels affecting millions of individuals worldwide. According to the center for disease control (CDC) reports, each death out of four is related to heart diseases, leading to approximately 610,000 mortalities annually worldwide (1). Among the heart diseases, CAD is the most common, responsible for the death of 370,000 individuals annually worldwide (1). CAD occurs when the elasticity of arteries, as well as vein and vessel smoothing, become plaque in the inner wall, making them rigid and narrowed. This condition restricts the blood flow to the heart muscle, leading to oxygen starvation. The condition of plaque rupture leads to the heart failure or cardiac death (2).
Recently, there has been an increase in the incidence of CHD (also known as ischemic heart disease) in China (3). In addition, CHD has become the most common reason for death in middle and high-income countries (4). According to the data report by NHANES, CHD prevalence was higher in males than females across all ages (7.4 v/s 5.3%, respectively) (3). The American Heart Association explains ‘The important difference between sex and pathology’, clinical presentation and outcomes in CHD patients (5). Thus it is crucial to pay attention to sex disparities and subsequently to personalize treatment (6). Patients with CHD are also susceptible to more complicated clinical problems. Currently, the diagnosis and therapy of CHD is rare and costly as compared to coronary angiography, which is the most popular clinical management option (7). CHD is one of the leading causes of death, and markedly affects the immunity of the body, making it an economic burden worldwide (8,9). This is a complex disease involving multiple mechanisms and influenced by many risk factors, including physical activity, genetics, diet, and smoking (10,11). Recently, a genome-wide association study (GWAS) identified many candidate loci associated with CHD and myocardial infection (MI) (12-14). Although genetics play an important role, accounting for approximately 50% of CHD heritability, the exact mechanism and causative agent of CHD are not yet revealed clearly (15-17). In this regard, it is important to understand and address the candidate genome association in developing CHD.
MicroRNAs (miRNA) are small noncoding RNAs with a length of 22-25 nucleotide and which play a key role in the regulation of gene expression and have implications in many human disorders (18), including many biological processes such as cell differentiation, proliferation and apoptosis (19-21). To the best of the knowledge of the authors, the association pattern of miRNAs to CHD is lacking, leading to demand for specific CHD patients. Although relevant research has been undertaken to address DEGs associated with CHD, DEGs have only been used to check the expression pattern in case of CHD. In this study, we addressed the possible association of genes with CHD, which may be useful for the diagnosis and treatment of this disease in the near future. Additionally, analysis of gene expression data and network analysis were performed to gain a better understanding of CHD for the identification of differentially expressed genes (DEGs), biomarkers and therapeutic target options.
Materials and methods
Data availability
To identify key genes for the development of CHD biomarkers, we used gene expression datasets of 4 angiographically proven patients who were being treated for more than 3 months or from group-1 (n=100) compared to healthy control (n=50). This dataset was downloaded from the GEO module of National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE56885). Microarray gene expression profiles were downloaded and further analyzed for the identification of DEGs. In this dataset, GSM1370681 and GSM1370682 represent the replicate samples of healthy individuals and GSM1370683 to GSM1370686 of four patients as baseline associated with CAD.
Differential Expression Analysis (DEG)
Using the default parameters, WEGO 2.0, and GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) were used to analyze the GEO series (22). The Benjamini and Hochberg false discovery rate method was utilized to adjust the P-values. NCBI-generated annotations were employed to display the DEG list by comparing the overall common gene expression pattern as compared to the control. On the basis of this analysis, possible associations related to CHD were reported. Although inappropriate to consider the data for analysis on inter-datasets, the average value of LogFC for all four datasets was assessed to represent the expression level.
Gene ontology (GO) analysis
The major bioinformatics tool GO was used as an initiative to understand the function of genes and gene products of Homo sapiens. The PANTHER (Protein ANalysis THrough Evolutionary Relationships) classification database (23) was used to perform the GO analysis, and the pathway analysis was performed using Reactome (24).
Protein-Protein Interaction (PPI) network construction analysis
An online freely available software package, STRING, was utilized to establish the PPI network (25), and all the cut-off points were combined to analyze the topology property of networks. Gene edges of >15 degrees were defined as hub genes.
Results and Discussion
Screening of differentially expressed common genes from microarray data sets
Atherosclerosis is one of the leading causes of cardiovascular diseases such as CHD (26). Understanding of the key players in expression, regulation and function, of GWAS CHD genes will provide the options to treat this disease, leading to further developments of novel therapeutic interventions (27). In this study, the first compressive investigation was conducted to identify the expression profile of collected microarray data sets of CHD. The dataset of two controls in replicate and four baseline test samples were used. We report an overall expression and function of genes associated with different biological processes, which may lead to CHD during pathological conditions. The overall study design is shown in Fig. 1, which presents CHD data of Homo sapiens from the GEO database, with four series of test samples and two control study sets. First, we used WEGO to visualize the GO annotations and the percentage of genetic association of different functions in cells to address the possible association with CHD.
A total of 52,998 genes sharing different functions such as cellular (18,476), biological (17,307) and molecular (17,215) functions were identified
Out of those, the highest gene association to cell, cell part, organelle, organelle part, membrane, binding, cellular process, biological regulation, and metabolic were topmost in the metadata of the CHD-associated data set (Fig. 2). The different GO representing the 0-90% range of gene expression as compared to control data set is shown in Fig. 3. The principal findings of this study confirm the association of immune system, inflammation, and apoptosis as mediators in the development of CHD. The impact of the immune system plays a key role in the development of heart failure. A transcriptomic study reported the sustained activation of the adoptive immune system which may be a contributing factor in the progression of CHD (28). Another report suggests that the imbalance in inflammatory and anti-inflammatory cytokines may lead to the onset of extensive fibrosis (29).
From the GEO database, accession GSE56885 of CSD patients, who were being treated for more than 3 months was selected
From the included patients, two controls in replicate and four overall test samples were used to consolidate data refining. The GEO2R was used to analyze the control and test data series by normalizing the microarray data for high quality. DEGs with different fold change confirm their crucial role in CHD (Table I).
In our study, many immune response processes were significantly changed and DEGs are associated with the metabolic process, which is associated with CHD
The CHDs of the innate immune system were largely mediated through neutrophils and monocyte, and macrophages (30), to contribute to the process of the chronic inflammation process.
Functional enrichment and unified DEG analysis
To precisely understand the gene changes during CHD, the DEGs GO was performed using the online PANTHER database for high-throughput analysis to classify the proteins and their genes into family and subfamily, molecular function, biological process, and pathway (31). In the dataset analyzed, the two significant changes in molecular function were protein binding (75%) and catalytic activity (56%), followed by molecular regulator, molecular transducer activity, structural activity, transcription regulator activity, and transporter activity (Fig. 4A). In terms of the biological process, the three most significant classes of CHD were cellular process (83%), biological regulation (57%), and metabolic process (44%) (Fig. 4B). Additionally, in terms of cellular components, another two more significant components are cell (65%) and organelle (58%), which were found to be associated with CHD (Fig. 4C). Many other target-associated DEGs were involved in the biological process, molecular function and cellular components.
Analyzed potential DEGs of the CHD data set shows protein classes distributed among transcription factor (24%), enzyme modulator (20%), nucleic acid binding (18%), and signaling molecules (18%) (Fig. 5A)
The DEGs mainly associated with CHD key pathways showed the significance are inflammation mediated by chemokine and cytokine signaling pathway (11%), CCKR signaling map (11%), gonadotropin-releasing hormone receptor pathway (8%), apoptosis signaling pathway (6%), and p53 pathway (5%) (Fig. 5B). This result was consistent with GO analysis, confirming the classes of proteins associated with CHD. Many genes associated with inflammatory roles, and a previous study showed a conserved signature of dilated cardiomyopathy (DCM) plays an important role in cell survival promotion during end-stage of heart failure (32). In the present study, we also revealed the expression pattern of apoptotic or inflammatory genes (Fig. 4) (33,34).
Pathway analysis
To address the overview of data insight into the pathways, which are associated and connected for CHD development (35), we analyzed 164 DEGs involved in different functional pathways compared to reference and expected genes for those pathways. A total of 13 pathways were found to be associated with signaling-, immune-, and transcription-related pathways (36). Genes were confirmed in the uploaded list over the expected one (number in the list divided by the expected number). If >1, it indicated that the category is over-represented in the experiment. Conversely, the category is under-represented if <1. In the future, overexpressed genes are likely to serve as the marker selected in the development of CHD interventions. The P-value indicates the Fisher's exact test (37) or Binomial statistic in which the probability is the number of genes observed in this category occurred by chance (randomly), as determined by the reference list (Table II).
PPI analysis
To address the PPI of the CHD dataset in this study, STRING online suits was used to address the possible interaction of protein of CHD associated DEGs. A total of 112 nodes, 257 edges, 4.59 average node edge, 0.387 average clustering coefficient, 77 expected edge number, and <1.0e-16 PPI enrichment value were observed, and shown the network was significantly interacted than expected. Previous studies investigated the rare variants through targeted expression profiling across CHD relevant tissues from appropriate cases and controls (38,39). The PPI indicates the interaction of genes associated with multiple genes for outcome. In the present study, we identified 422 GO for biological process, 31 GO for molecular function, 12 GO for cellular component, 33 pathways, 30 reactome pathways, 13 UniProt keywords, 11 PFAM protein domains, 29 INTERO protein domains, and 3 SMART protein domains in the analysis of CHD microarray data set. In those findings, associated edges shows physically binding protein and some of them were associated with but did not have physical binding. Of these, only the top-ranking ones have been presented (Fig. 6 and Table III).
Understanding and ruling the mechanism
There are several challenges to identifying the genetic basis of CHD that are also the determinants of this complex disease, including phenotypic and genetic heterogeneity, gene-environment, and etiological spectrum range and their effect. Considering research efforts involved in determining the genetic basis of this CHD, there is a need to understand the fine complexity of genetic association leading to mortality in developing countries. There is a need to focus on clinical manifestation rather than factors which influence or are heritable by genetic factors. There are many challenges in determining the genetic association of CHDs, such as phenotypic heterogeneity, genetic heterogeneity, small gene effects, gene-gene and gene-environment interactions and rare variants causing complex diseases. Some of the key points to be undertaken such as mortality, challenge in identifying the genetic determinants, studying linkage mapping through conventional approaches, and cataloguing of human diseases variation at single-nucleotide polymorphism (SNP), as well as genotyping will increase the likelihood of success.
In conclusion, we studied a comprehensive gene expression profile of microarray data of CHD. During the progression of CHD, there was a significant change in the expression of genes involved in the immune system, inflammation, and cell signaling through protein binding. This analysis provides valuable information for future research and in understanding the mechanism of CHD as well as identification of novel interventions for therapeutic application.
Acknowledgements
Not applicable
Funding
This research received no specific grants from any funding agencies.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Authors' contributions
ZY conceived and designed the study. WL provided study materials. ZY, HM and WL were responsible for the collection and assembly of data, data analysis and interpretation. ZY was involved in writing the manuscript. All authors read and approved the final manuscript.
Ethics approval and consent to participate
Not applicable
Patient consent for publication
Not applicable
Competing interests
The authors declare that they have no competing interests.
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