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Bioinformatics and System Biological Approaches for the Identification of Genetic Risk Factors in the Progression of
Joy Dip Barua1, Shudeb Babu Sen Omit2, Humayan Kabir Rana3
1Department of Pharmacy, BGC Trust University Bangladesh, Chattogram, Bangladesh.
Insights
This study reveals the genetic links between cardiovascular disease (CVD) and its risk factors like hypertension and diabetes. Findings identify key genes and pathways, aiding future CVD treatment strategies.
Area of Science:
- Genomics
- Computational Biology
- Cardiovascular Research
Background:
- Cardiovascular disease (CVD) is a leading global cause of mortality, accounting for one-third of annual deaths worldwide.
- Established risk factors exacerbate CVD, but their genetic associations remain underexplored in current literature.
- This study investigates the intricate genetic linkages between CVD and its primary risk factors.
Purpose of the Study:
- To computationally explore the genetic associations between cardiovascular disease (CVD) and its key risk factors.
- To identify shared differentially expressed genes (DEGs), hub proteins, and biological pathways implicated in CVD.
- To provide a foundation for further laboratory validation and the development of novel CVD therapeutic strategies.
Main Methods:
- Utilized GEO microarray datasets to analyze gene expression patterns in CVD and associated risk factors.
- Performed diseasome, protein-protein interaction (PPI), and pathway analyses to uncover molecular relationships.
- Validated findings using established databases such as OMIM, dbGAP, and DisGeNET.
Main Results:
- Identified overlapping DEGs between CVD and risk factors: hypertension (32), type 2 diabetes (17), hypercholesterolemia (53), obesity (70), and aging (89).
- Discovered 10 major hub proteins (e.g., FPR2, TNF, CXCL8) and significant functional/gene ontological pathways associated with CVD.
- Confirmed the genetic connections between CVD and its risk factors through gold benchmark databases.
Conclusions:
- The computational approach successfully elucidated the genetic associations between CVD and its risk factors.
- Identified significant DEGs, hub proteins, and pathways provide a basis for understanding CVD pathogenesis.
- These findings offer potential targets for future laboratory research and the development of effective CVD treatments.
Background:
Cardiovascular disease (CVD) is the combination of coronary heart disease, myocardial infarction, rheumatic heart disease, and peripheral vascular disease of the heart and blood vessels. It is one of the leading deadly diseases that causes one-third of the deaths yearly in the globe. Additionally, the risk factors associated with it make the situation more complex for cardiovascular patients, which lead them towards mortality, but the genetic association between CVD and its risk factors is not clearly explored in the global literature. We addressed this issue and explored the linkage between CVD and its risk factors.
Methods:
We developed an analytical approach to reveal the risk factors and their linkages with CVD. We used GEO microarray datasets for the CVD and other risk factors in this study. We performed several analyses including gene expression analysis, diseasome analysis, protein-protein interaction (PPI) analysis, and pathway analysis for discovering the relationship between CVD and its risk factors. We also examined the validation of our study using gold benchmark databases OMIM, dbGAP, and DisGeNET.
Results:
We observed that the number of 32, 17, 53, 70, and 89 differentially expressed genes (DEGs) is overlapped between CVD and its risk factors of hypertension (HTN), type 2 diabetes (T2D), hypercholesterolemia (HCL), obesity, and aging, respectively. We identified 10 major hub proteins (FPR2, TNF, CXCL8, CXCL1, IL1B, VEGFA, CYBB, PTGS2, ITGAX, and CCR5), 12 significant functional pathways, and 11 gene ontological pathways that are associated with CVD. We also found the connection of CVD with its risk factors in the gold benchmark databases. Our experimental outcomes indicate a strong association of CVD with its risk factors of HTN, T2D, HCL, obesity, and aging.
Conclusions:
Our computational approach explored the genetic association of CVD with its risk factors by identifying the significant DEGs, hub proteins, and signaling and ontological pathways. The outcomes of this study may be further used in the lab-based analysis for developing the effective treatment strategies of CVD.
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