Acute myocardial infarction (AMI), which represents the main public health issue around the world, is a common cardiac emergency with substantial morbidity and mortality. In the last two or three decades, although a downtrend of AMI has been observed because of the economic development and advances in medical science, its morbidity is still very high at about 44.57 in 100,000 people in China in 2013 [
Fortunately, with the development of gene chip technique, more and more gene expression spectra were tested by gene chip technique in cardiovascular clinic and study. Microarray analysis was widely used in peripheral blood of patients with myocardial infarction [
In this research, three gene expression datasets, including GSE775, GSE19322, and GSE97494, were downloaded from the GEO database. These datasets were screened to identify the DEGs in each dataset. Next, using the RRA approach [
Utilizing the keywords “myocardial infarction,” we screened the GEO database. Three GEO datasets were found, including GSE775 contributed by Schinke et al., GSE19322 contributed by Hunt et al., and GSE97494 contributed by Chikata et al. These gene expression profiles of AMI were downloaded based on GPL81 platform of Affymetrix Murine Genome U74A Version 2 Array, GPL339 platform of Affymetrix Mouse Expression 430A Array, and GPL6246 platform of Affymetrix Mouse Gene 1.0 ST Array, respectively. There were 18 samples that were from the region between the LAD artery and the apex of the mice, 9 mice within 24 hours after AMI and 9 sham-operated mice within 24 hours. Detailed information about the datasets is listed in Table
Details for GEO AMI data.
Reference | Sample | GEO | Platform | AMI | sham-operation |
---|---|---|---|---|---|
Schinke et al. (2003) | LV myocardium | GSE775 | GPL81 | 3 | 3 |
Hunt et al. (2010) | LV myocardium | GSE19322 | GPL339 | 4 | 4 |
Chikata et al. (2017) | LV myocardium | GSE97494 | GPL6246 | 2 | 2 |
Abbreviation: GEO, Gene Expression Omnibus. AMI, acute myocardial infarction. LV, left ventricular.
In order to find out DEGs of each GEO dataset, utilizing the R software and annotation package, the platform and series matrix file(s) were converted. These DEGs in AMI and sham operation group samples were analyzed by utilizing the limma package [
Through limma packet analysis, we obtained the list of DEGs of the three microarray datasets. The list of down- and upregulated genes in the microarray data was saved. Subsequently, using the RRA approach, the comparison of multiple ranked gene lists was performed.
Biological functions of the DEGs obtained from the integration of microarray data were explored with GO analysis using Clusterprofiler which is an R package utilized to compare the biological themes among gene clusters. Similarly, in order to identify the enrichment signaling pathways of DEGs, KEGG pathway analysis was performed by utilizing the Clusterprofiler package. A corrected
In order to identify the interaction between PPI, the PPI network was built using the STRING (version 11) online database. The highest confidence of the argument of interactions was set at >0.4. To draw an interaction of DEGs, the Cytoscape (version 3.6.1) software was used to visualize and analyse the PPI network. In order to find modules of the whole network, the Molecular Complex Detection (MCODE) plug-in of the Cytoscape software was applied. The hub genes were identified by using the plug-in cytoHubba [
Three expression microarray datasets, including GSE775, GSE19322, and GSE97494, were used to perform background correction and quartile data normalization by the limma package. Meanwhile, using the limma package (log2FC >1, corrected
Volcano plot of gene expression profile data in AMI samples and sham-operation ones and correlation coefficient analysis diagram. Notes: (a) volcano plot of GSE775, (b) volcano plot of GSE19322, and (c) volcano plot of GSE97494. The red, green, and gray points represent upregulated genes, downregulated genes, and nondifferentially expressed genes, respectively. They are screened on the basis of log2FC >1.0 and a corrected p <0.05. (d) Correlation coefficient analysis diagram. Each column and row, respectively, represents one sample. Red represents strong correlation between samples and grey represents weak correlation between samples. Different shades of colors indicate the different correlation degree. Abbreviation: AMI, acute myocardial infarction; DEGs, differentially expressed genes; FC, fold change.
Hierarchical clustering heatmap of DEGs, which was screened on the basis of log2FC >1.0 and a corrected p <0.05. Notes: (a) GSE775 data, (b) GSE19322 data, and (c) GSE97494 data. Red represents that the expression of genes is relatively upregulated. Blue represents that the expression of genes is relatively downregulated. Gray represents the expression of genes without significant changes. Abbreviation: DEGs, differentially expressed genes; FC, fold change.
Using the RRA method according to Log2FC >1 and a corrected
Screening DEGs in AMI by integrated microarray.
DEGs | Gene names |
---|---|
Upregulated |
|
Downregulated |
|
Abbreviation: DEGs, differentially expressed genes.
The heatmap of differentially expressed genes. Notes: each column and row represents one dataset and one gene, respectively. Red and green represent logFC >0 and logFC <0, respectively. The logFC values are shown in each rectangle. The gradual color ranged from green to red represents the changing process from downregulation to upregulation. Abbreviation: FC, fold change.
Using Clusterprofiler package, biological annotation of the DEGs obtained by RRA approach was performed. The down- and upregulated genes with
GO analysis of genes associated with AMI.
Term | Description | Count |
|
---|---|---|---|
GO:0048018 | receptor ligand activity | 13 | 8.99E-11 |
GO:0005125 | cytokine activity | 9 | 2.63E-09 |
GO:0008009 | chemokine activity | 5 | 5.77E-08 |
GO:0005126 | cytokine receptor binding | 9 | 7.10E-08 |
GO:0042379 | chemokine receptor binding | 5 | 5.14E-07 |
GO:0045236 | CXCR chemokine receptor binding | 3 | 4.59E-06 |
GO:0008083 | growth factor activity | 5 | 2.57E-05 |
GO:0030246 | carbohydrate binding | 6 | 4.20E-05 |
GO:0001664 | G-protein coupled receptor binding | 6 | 6.03E-05 |
GO:0050542 | icosanoid binding | 2 | 0.000248517 |
GO:0050543 | icosatetraenoic acid binding | 2 | 0.000248517 |
GO:0070851 | growth factor receptor binding | 4 | 0.000399461 |
GO:0035325 | Toll-like receptor binding | 2 | 0.000428787 |
GO:0036041 | long-chain fatty acid binding | 2 | 0.000743064 |
GO:0070492 | oligosaccharide binding | 2 | 0.000743064 |
GO:0016209 | antioxidant activity | 3 | 0.000961581 |
GO:0005539 | glycosaminoglycan binding | 4 | 0.001232506 |
GO:0019956 | chemokine binding | 2 | 0.00149192 |
GO:0051787 | misfolded protein binding | 2 | 0.00149192 |
GO:0001530 | lipopolysaccharide binding | 2 | 0.001619175 |
GO:0031406 | carboxylic acid binding | 4 | 0.001701432 |
GO:0043177 | organic acid binding | 4 | 0.001850987 |
GO:0019955 | cytokine binding | 3 | 0.002141304 |
GO:0001968 | fibronectin binding | 2 | 0.002329934 |
GO:1901567 | fatty acid derivative binding | 2 | 0.002815131 |
GO:0044183 | protein binding involved in protein folding | 2 | 0.003914772 |
GO:0031072 | heat shock protein binding | 3 | 0.004127547 |
GO:0005504 | fatty acid binding | 2 | 0.004741625 |
GO:0008201 | heparin binding | 3 | 0.005037273 |
GO:0048020 | CCR chemokine receptor binding | 2 | 0.005409964 |
GO enrichment analyses of DEGs in AMI. Notes: X-axis indicates the percentage that each functional group gene, respectively, accounts for the total genes. Y-axis represents different functional groups (also named as different GO terms). The size of the dot indicates the number of genes in different functional groups, and the color of the dot reflects the different
Top 20 KEGG pathway analyses of DEGs are shown in Table
Top 20 KEGG pathway enrichment analyses of DEGs associated with AMI.
pathway | ID | Gene count |
|
adjust |
Genes |
---|---|---|---|---|---|
IL-17 signaling pathway | mmu04657 | 9 | 2.32E-10 | 3.30E-08 |
|
Legionellosis | mmu05134 | 7 | 6.85E-09 | 4.87E-07 |
|
Cytokine-cytokine receptor interaction | mmu04060 | 10 | 6.80E-07 | 3.22E-05 |
|
Salmonella infection | mmu05132 | 6 | 1.38E-06 | 4.88E-05 |
|
TNF signaling pathway | mmu04668 | 6 | 1.03E-05 | 0.000292216 |
|
Rheumatoid arthritis | mmu05323 | 5 | 3.97E-05 | 0.00093865 |
|
Hematopoietic cell lineage | mmu04640 | 5 | 7.17E-05 | 0.001297709 |
|
Malaria | mmu05144 | 4 | 7.31E-05 | 0.001297709 |
|
Toll-like receptor signaling pathway | mmu04620 | 5 | 8.73E-05 | 0.001377696 |
|
Chagas disease (American trypanosomiasis) | mmu05142 | 5 | 0.000105431 | 0.001497126 |
|
Amoebiasis | mmu05146 | 5 | 0.000120813 | 0.001559588 |
|
Human cytomegalovirus infection | mmu05163 | 7 | 0.000153378 | 0.00181497 |
|
Transcriptional misregulation in cancer | mmu05202 | 6 | 0.000179856 | 0.001964578 |
|
Chemokine signaling pathway | mmu04062 | 6 | 0.000291266 | 0.002954267 |
|
MAPK signaling pathway | mmu04010 | 7 | 0.000358914 | 0.003397722 |
|
Pertussis | mmu05133 | 4 | 0.000404457 | 0.003589552 |
|
Kaposi sarcoma-associated herpesvirus infection | mmu05167 | 6 | 0.000451092 | 0.003767942 |
|
Prion diseases | mmu05020 | 3 | 0.00050479 | 0.003982228 |
|
AGE-RAGE signaling pathway in diabetic complications | mmu04933 | 4 | 0.001183968 | 0.008848603 |
|
HIF-1 signaling pathway | mmu04066 | 4 | 0.001367781 | 0.009711245 | Serpine1,Il6,Hmox1,Slc2a1 |
Top 20 KEGG pathway enrichment analyses of DEGs in AMI. Notes: X-axis indicates gene count; Y-axis represents different pathways. The column color reflects
In order to ulteriorly explore the biological characteristics of these DEGs, a PPI network was created using the STRING database. There were 56 nodes and 240 edges in this network, including 2 down- and 54 upregulated genes (see the supplementary document (available
Establishment of PPI network, modules analysis, and Venn diagram. (a) Whole PPI network. Circles and lines represent genes and the interaction of proteins between genes, respectively. The red represents the upregulated genes. The green represents the downregulated genes. (b) PPI network of module. Circles and lines represent genes and the interaction of proteins between genes, respectively. The red represents the upregulated genes. (c) Venn diagram of mutual hub genes based on two methods. Abbreviation: PPI, protein-protein interaction.
AMI is one of the common kinds of coronary heart disease with high morbidity and mortality all over the world. In recent years, the number of patients with AMI is increasing annually. Controlling the number of patients with AMI and exploring the molecular mechanism of AMI are urgent to be solved.
In the study, using integrated bioinformatics and RRA analysis method, a total of 57 DEGs, including 2 down- and 55 upregulated genes, were identified from the GSE775, GSE19322, and GSE97494 database. From GO functional enrichment analysis, we identified that these DEGs were mainly enriched in the following functional categories, including receptor ligand activity, cytokine activity, cytokine receptor binding, G-protein coupled receptor binding, carbohydrate binding, chemokine activity, and chemokine receptor binding. Through KEGG pathway enrichment analysis, we found that the DEGs were chiefly enriched in the pathway of cytokine-cytokine receptor interaction, MAPK signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway, and chemokine signaling pathway. Utilising the STRING database, the PPI network was constructed. The module analysis filtered out 18 key genes, including
Chemokine (C-C motif) receptor 1 (
Prostaglandin-endoperoxide synthase 2 (
Among these genes, a novel gene
Wei Gong et al. [
It is noticeable that there have been papers researching the differentially expressed genes in AMI. However, the results of those papers were somewhat different from ours. The following reasons may account for this phenomenon: (1) some studies [
In conclusion, our study provides an integrated bioinformatics analysis of DEGs of AMI. This research provides numerous genes associated with AMI. This study may provide credible molecular biomarkers in terms of screening, diagnosis, and prognosis for AMI. Meanwhile, it also serves as a basis for exploring new therapeutic target for AMI. Compared with other studies of AMI, innovation point and merit of our current study was that the RRA method was utilized for the first time in exploring DEGs in AMI study. This study also has certain limitations. In this study, 18 microarrays were only screened, which is not enough. The limited sample size may easily lead to false positive results. Therefore, to verify the current findings, it is necessary to perform more experiments.
The data used to support the findings of this study are included within the supplementary information file.
The authors declare that they have no conflicts of interest.
This study was funded by the Project of Young and Middle-aged Talent Cultivation of Fujian Provincial Health System, China (Grant No. 2013-ZQN-JC-30).
A PPI network: there were 56 nodes and 240 edges in this network, including 2 down- and 54 upregulated genes (see the supplementary document).