ArticleScientific reports2022
Integrative network analysis interweaves the missing links in cardiomyopathy diseasome.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 11 citations in OpenAlex.
- Epigenetics, Modifiers, and Molecular Noise: Rethinking Pathophysiology in Dilated and Hypertrophic Cardiomyopathies.International journal of molecular sciences · 2026Review
- Disease gene prioritization with quantum walks.Bioinformatics (Oxford, England) · 2024Article
- Multicentric Assessment of a Multimorbidity-Adjusted Disability Score to Stratify Depression-Related Risks Using Temporal Disease Maps: Instrument Validation Study.Journal of medical Internet research · 2024Article
- Transcriptome data analysis of primary cardiomyopathies reveals perturbations in arachidonic acid metabolism.Frontiers in cardiovascular medicine · 2023Article
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Cardiomyopathies are progressive disease conditions that give rise to an abnormal heart phenotype and are a leading cause of heart failures in the general population. These are complex diseases that show co-morbidity with other diseases. The molecular interaction network in the localised disease neighbourhood is an important step toward deciphering molecular mechanisms underlying these complex conditions. In this pursuit, we employed network medicine techniques to systematically investigate cardiomyopathy's genetic interplay with other diseases and uncover the molecular players underlying these associations. We predicted a set of candidate genes in cardiomyopathy by exploring the DIAMOnD algorithm on the human interactome. We next revealed how these candidate genes form association across different diseases and highlighted the predominant association with brain, cancer and metabolic diseases. Through integrative systems analysis of molecular pathways, heart-specific mouse knockout data and disease tissue-specific transcriptomic data, we screened and ascertained prominent candidates that show abnormal heart phenotype, including NOS3, MMP2 and SIRT1. Our computational analysis broadens the understanding of the genetic associations of cardiomyopathies with other diseases and holds great potential in cardiomyopathy research.
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Registered trials
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