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Research article
Myocardial Infarction Biomarker Discovery with Integrated Gene Expression, Pathways and Biological Networks analysis
Abdulrahman MUJALLI
King Abdulaziz University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-8630-3963
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Myocardial infarction (MI) is the most prevalent coronary atherosclerotic heart disease caused by the complex molecular interactions between multiple genes and environment. Molecular exploration of gene expression changes in MI patients is very crucial not just to understand the molecular basis of disease development but also to identify potential therapeutic targets. Therefore, we aim to identify potential biomarkers for the disease development mechanisms and for prognosis of MI using extensive integrated biological network analysis.
Methodology
Gene expression datasets (GSE66360) generated from 51 healthy controls and 49 endothelial cell samples from patients experiencing acute MI were used to analyze the differentially expressed genes (DEG), protein-protein interactions (PPI), gene network-clusters to annotate the candidate pathways relevant to MI pathogenesis.
Results
Bioinformatic analysis revealed 810 DEGs, between control and MI samples, with 574 up- and 236 down- regulated genes. Their functional annotations with Gene Ontology (GO) has captured several MI targeting biological processes like immune response, inflammation and platelets degranulation. Most significantly DEGs enriched KEGG pathways are related to the following functions: Cytokine-cytokine receptor interaction, TNF and NFkB signaling. By constructing the PPI network using STRING and CytoHubba, seventeen hub and bottleneck genes were found, whose involvement in MI was further confirmed by DisGeNET data. Search in the Open Target Platform reveal unique bottleneck genes as potential target for MI.
Conclusion
Our integrative bioinformatics analysis of large-scale gene expression data has identified several potential genetic biomarkers associated with early stage MI providing a new insight into molecular mechanism underlying the disease.
Keywords
bioinformatics, GEO, myocardial infarction, differentially expressed genes, immune response, inflammation response, biomarkers
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This is a preprint. It has not completed peer review.
Research article
Myocardial Infarction Biomarker Discovery with Integrated Gene Expression, Pathways and Biological Networks analysis
Abdulrahman MUJALLI
King Abdulaziz University
Corresponding Author
ORCiD: https://orcid.org/0000-0002-8630-3963
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
History
CURRENT STATUS: Posted
Version 1
Posted 15 Jun, 2020
No community comments so far
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Myocardial infarction (MI) is the most prevalent coronary atherosclerotic heart disease caused by the complex molecular interactions between multiple genes and environment. Molecular exploration of gene expression changes in MI patients is very crucial not just to understand the molecular basis of disease development but also to identify potential therapeutic targets. Therefore, we aim to identify potential biomarkers for the disease development mechanisms and for prognosis of MI using extensive integrated biological network analysis.
Methodology
Gene expression datasets (GSE66360) generated from 51 healthy controls and 49 endothelial cell samples from patients experiencing acute MI were used to analyze the differentially expressed genes (DEG), protein-protein interactions (PPI), gene network-clusters to annotate the candidate pathways relevant to MI pathogenesis.
Results
Bioinformatic analysis revealed 810 DEGs, between control and MI samples, with 574 up- and 236 down- regulated genes. Their functional annotations with Gene Ontology (GO) has captured several MI targeting biological processes like immune response, inflammation and platelets degranulation. Most significantly DEGs enriched KEGG pathways are related to the following functions: Cytokine-cytokine receptor interaction, TNF and NFkB signaling. By constructing the PPI network using STRING and CytoHubba, seventeen hub and bottleneck genes were found, whose involvement in MI was further confirmed by DisGeNET data. Search in the Open Target Platform reveal unique bottleneck genes as potential target for MI.
Conclusion
Our integrative bioinformatics analysis of large-scale gene expression data has identified several potential genetic biomarkers associated with early stage MI providing a new insight into molecular mechanism underlying the disease.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9