ArticleCardiovascular therapeutics2022
Bioinformatics and System Biological Approaches for the Identification of Genetic Risk Factors in the Progression of Cardiovascular Disease.
Article in Cardiovascular therapeutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Cytokine-Family Biomarker Candidates for Small Abdominal Aortic Aneurysm Identified via Integrated mRNA and Protein Expression Profiling.International journal of molecular sciences · 2026Article
- G protein-coupled receptors and inflammation resolution signaling networks in the heart: Pharmacology and potential for innovative therapeutics.Pharmacological reviews · 2026Review
- Protective effects of melatonin on stroke in diabetic mice: central and peripheral inflammation modulation.Stroke and vascular neurology · 2025Article
- Spectral divergence prioritizes key classes, genes, and pathways shared between substance use disorders and cardiovascular disease.Frontiers in neuroscience · 2025Article
- MALAT1 and HIST1H2BC as connecting diagnostic biomarkers in Ebola and its overlapping disease: An integrative approach.Bioinformation · 2025Article
- Influence of multi-species data on gene-disease associations in substance use disorder using random walk with restart models.PloS one · 2025Article
- Comprehensive bioinformatics and machine learning analyses for breast cancer staging using TCGA dataset.Briefings in bioinformatics · 2024Article
- Integrative Bioinformatics Approaches to Uncover Hub Genes and Pathways Involved in Cardiovascular Diseases.Cell biochemistry and biophysics · 2024Article
- A bioinformatics approach to elucidate conserved genes and pathways in C. elegans as an animal model for cardiovascular research.Scientific reports · 2024Article
- Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.Journal of Korean medical science · 2023Review
- Identifying Cardiovascular Disease Risk Factors in Adults with Explainable Artificial Intelligence.Anatolian journal of cardiology · 2023Article
- Identification of Comorbidities, Genomic Associations, and Molecular Mechanisms for COVID-19 Using Bioinformatics Approaches.BioMed research international · 2023Article
- Bioinformatics analysis of aging-related genes in thoracic aortic aneurysm and dissection.Frontiers in cardiovascular medicine · 2023Article
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Authors and funding
6 authors.
Funding
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Abstract
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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