Stress cardiomyopathy, also known as takotsubo cardiomyopathy (TTC), was first reported in Japan in 1990 [
The exact pathophysiology of stress cardiomyopathy is unknown and seems to be associated with excess plasma catecholamine, which is caused by stress conditions. Myocardial ischemia appears to play a crucial role in takotsubo syndrome, both in human heart muscle specimens and in experimental models of SCMP. Most cases occur in patients with risk factors for endothelial dysfunction [
Most patients with stress-induced cardiomyopathy (>95%) have electrocardiogram abnormalities that typically show ischemic ST-segment and T-wave changes, but his appearance is most likely to associate the presence of ACS than SCM [
In this study, we further revealed biomarkers related to SCM by analyzing gene expression profile (GSE95368) deposited by Yvonne Edwards et al. (2017). Identifying key genes and pathways contributes to a better understanding of the pathophysiological mechanism of disease development, which provides innovative ideas for the diagnosis and treatment of SCM.
Gene expression data of GSE95368 is available for download from the NCBI Gene Expression Omnibus (GEO;
Genetic matrix files and platform files were downloaded to eliminate errors and make the experimental group comparable between control groups, so the obtained data were standardized. The estimation package is based on the KNN (k-nearest neighbor) algorithm is used to fill the missing values. After this, the probes were converted into gene symbols on the basis of the annotation platform file. If there are multiple probes corresponding to a gene, take the average as the final value. If the probe without gene symbol was removed, the normalized between array function in the limma package is applied to standardize the data. Then, the expression data were log2 transformed and the limma functional package in R software was used to compare gene expression in SCM and control samples to identify DEGs. The screening criteria for DEGs were
Database for Annotation, Visualization and Integrated Discovery (DAVID) (
PPI network analyses can show the functional link between proteins and proteins, using string software (
Molecular Complex Detection (MCODE) can find the interacting dense region in the PPI network, and the dense regions of interest can also be extracted and visualized. So we use MCODE to discover modules across the network. The hub genes were identified by using the plug-in cytoHubba of the Cytoscape software, including Maximal Clique Centrality (MCC), Density of Maximum Neighborhood Component (DMNC), and Maximum Neighborhood Component (MNC).
The results of standardizing the matrix file are shown in Figure
Standardization of gene expression. The blue bar represents the data before normalization, and the red bar represents the normalized data.
Screening upregulated and downregulated DEGs.
DEGs | Gene symbol |
---|---|
Upregulated (15) | LTA4H APOB ALB IL36A PRKACA NAMPT XPNPEP1 |
Downregulated (10) | GAPDH APOE CRP NPPB PKM2 GDF15 PLAT |
Abbreviation: DEGs: differentially expressed genes.
Heatmap results of DEGs. Abbreviation: DEGs, differentially expressed genes.
Differential expression of data between two sets of samples.
The GO analysis consists of biological processes (BP), cellular component (CC), and molecular function (MF) terms. The different genes with adjusted
GO enrichment analysis of differentially expressed genes.
Term | Description | Count | |
---|---|---|---|
GO:0005576 | Extracellular region | 7 | 4.42 |
GO:0051240 | Positive regulation of the multicellular organismal process | 5 | 5.94 |
GO:0051234 | Establishment of localization | 7 | 6.12 |
GO:0097708 | Intracellular vesicle | 5 | 8.56 |
GO:0098802 | Plasma membrane signaling receptor complex | 3 | 1.31 |
GO:1901576 | Organic substance biosynthetic process | 7 | 3.12 |
GO:0030198 | Extracellular matrix organization | 3 | 6.13 |
GO:0050794 | Regulation of cellular process | 8 | 6.88 |
GO:0032102 | Negative regulation of response to external stimulus | 3 | 9.39 |
GO:0008152 | Metabolic process | 8 | 1.10 |
GO:0032270 | Positive regulation of cellular protein metabolic process | 4 | 1.32 |
GO:0140096 | Catalytic activity, acting on a protein | 5 | 1.78 |
GO:0031399 | Regulation of protein modification process | 4 | 1.89 |
GO:0007229 | Integrin-mediated signaling pathway | 2 | 2.37 |
GO:0050810 | Regulation of steroid biosynthetic process | 2 | 2.37 |
GO:0120039 | Plasma membrane-bounded cell projection morphogenesis | 3 | 4.40 |
GO:0001568 | Blood vessel development | 3 | 4.80 |
GO:0030667 | Secretory granule membrane | 2 | 5.16 |
GO:1990266 | Neutrophil migration | 2 | 5.84 |
GO:0072562 | Blood microparticle | 2 | 7.34 |
Abbreviation: GO: Gene Ontology.
The DEGs significantly enriched GO (top 20). Abbreviation: DEGs, differentially expressed genes; GO, Gene Ontology.
The KEGG pathways of the DEGs were analyzed using DAVID and KOBAS. The top 20 of the KEGG pathways is shown in Table
KEGG pathway analysis of DEGs.
Pathway | ID | Count | Genes | |
---|---|---|---|---|
ECM-receptor interaction | hsa04512 | 5 | 4.03 | ITGA1|ITGB3|THBS2|ITGB1|ITGA2B |
Dilated cardiomyopathy (DCM) | hsa05414 | 5 | 6.84 | ITGA1|ITGB3|PRKACA|ITGB1|ITGA2B |
Human papillomavirus infection | hsa05165 | 6 | 8.06 | THBS2|ITGB1|ITGB3|PRKACA|ITGA1|ITGA2B |
Focal adhesion | hsa04510 | 5 | 2.31 | ITGA1|ITGB3|THBS2|ITGB1|ITGA2B |
Arrhythmogenic right ventricular cardiomyopathy (ARVC) | hsa05412 | 4 | 2.55 | ITGA1|ITGB3|ITGB1|ITGA2B |
Hypertrophic cardiomyopathy (HCM) | hsa05410 | 4 | 4.65 | ITGA1|ITGB3|ITGB1|ITGA2B |
Platelet activation | hsa04611 | 4 | 1.60 | ITGB3|PRKACA|ITGB1|ITGA2B |
Phagosome | hsa04145 | 4 | 3.52 | ITGB3|THBS2|ITGB1|C3 |
PI3K-Akt signaling pathway | hsa04151 | 5 | 3.71 | ITGA1|ITGB3|THBS2|ITGB1|ITGA2B |
Proteoglycans in cancer | hsa05205 | 4 | 1.08 | ITGB3|PRKACA|ITGB1|PTPN6 |
Regulation of actin cytoskeleton | hsa04810 | 4 | 1.32 | ITGA1|ITGB3|ITGB1|ITGA2B |
Leishmaniasis | hsa05140 | 3 | 1.89 | ITGB1|PTPN6|C3 |
Complement and coagulation cascades | hsa04610 | 3 | 2.29 | PLAT|A2M |C3 |
Hematopoietic cell lineage | hsa04640 | 3 | 4.15 | ITGA1|ITGB3|ITGA2B |
Fluid shear stress and atherosclerosis | hsa05418 | 3 | 0.000117572 | ITGB3|PLAT|ITGA2B |
Cholesterol metabolism | hsa04979 | 2 | 0.00056172 | APOE|APOB |
Thyroid hormone synthesis | hsa04918 | 2 | 0.001195438 | PRKACA|ALB |
Pertussis | hsa05133 | 2 | 0.001258577 | ITGB1|C3 |
Rap1 signaling pathway | hsa04015 | 3 | 0.00038722786 | ITGB3|ITGB1|ITGA2B |
ECM-receptor interaction | hsa04512 | 5 | 4.03 | ITGA1|ITGB3|THBS2|ITGB1|ITGA2B |
Abbreviation: KEGG: Kyoto Encyclopedia of Genes and Genomes; DEGs: differentially expressed genes.
KEGG pathway analysis of DEGs. Abbreviation: KEGG: Kyoto Encyclopedia of Genes and Genomes; DEGs: differentially expressed genes.
Use the string online tool to create a PPI network to gain a better understanding of the biological properties of DEGs. There were 24 nodes and 51 edges in this network, as shown in Figure
Results of PPI network analysis of DEGs. Abbreviation: PPI: protein-protein interaction; DEGs: differentially expressed genes.
PPI network of module. Abbreviation: PPI: protein-protein interaction.
Venn diagram of common hub genes based on three methods. Abbreviation: MCC: Maximal Clique Centrality; DMNC: Density of Maximum Neighborhood Component; MNC: Maximum Neighborhood Component.
Hub genes based on cytoHubba.
Projects | Methods in cytoHubba | ||
---|---|---|---|
MCC | MNC | DMNC | |
Gene symbol top 10 | |||
CRP | |||
CRP | |||
PLAT | |||
GAPDH | |||
IL36A | |||
GAPDH | |||
PLAT | NPPB | NPPB |
Bold gene symbols were the overlap hub gene. Abbreviation: MCC: Maximal Clique Centrality; DMNC: Density of Maximum Neighborhood Component; MNC: Maximum Neighborhood Component.
In this study, we performed an integrated analysis of gene expression profiles from serum samples without/with SCM aiming to identify the DEGs, related key signaling pathways, and hub genes for the disease. A total of 25 DEGs, including 10 upregulated and 15 downregulated genes, were identified from the GSE95368 database. The GO enrichment analysis showed that these differential genes associated with SCM were mainly enriched in the extracellular region, positive regulation of the multicellular organismal process, establishment of localization, and intracellular vesicle. From the KEGG pathway enrichment analysis, we identified that these DEGs were mainly enriched in the pathway of the ECM-receptor interaction and dilated cardiomyopathy (DCM). Through the construction and module analysis of the PPI network, we identified 9 key genes, including SAA1, C3, CRP, ALB, APOE, APOB, MFGE8, GAPDH, and PLAT. Finally, APOE, MFGE8, ALB, APOB, SAA1, A2M, and C3 are regarded as hub genes for the development of SCM.
MFGE8, a secreted glycoprotein, is associated with a variety of pathophysiological processes, including anti-inflammatory [
C3 is the most abundant complement component in serum, mainly macrophage and liver synthesis, and plays an important role in complementing the classical activation pathway and bypass activation pathway [
Alpha-2-macroglobulin (A2M) is a broad-spectrum protease-binding protein of the vertebrate innate immune system that prevents pathogen invasion [
Serum albumin (ALB) is synthesized in the liver and is the most abundant protein in vertebrate plasma. Its main function is to maintain plasma colloid osmotic pressure and participate in the transport of various substances [
Apolipoprotein is a protein that can bind and transport blood lipids to tissues for metabolism and utilization [
In this experiment, we analyzed gene chips to obtain SCM possible key genes and related pathway information. However, due to the defects of the study itself, the conclusion needs basic and clinical experimental verification. Most regrettably, due to the limited experimental conditions, the conclusions drawn in this paper cannot be further investigated. But we hope to be able to provide new ideas for SCM diagnosis based on this study and expect other scientific researchers to further explore this.
In summary, our study provides an integrated bioinformatics analysis of DEGs of SCM. In the present study, we identified some key genes and pathways. However, the key genes and signaling pathways related to SCM derived from this study still need further experimental verification due to the defects of analytical methods and sample size.
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.
GSE95368 datasets were downloaded from GEO (