ArticleJournal of molecular medicine (Berlin, Germany)2015
Integrated systems approach identifies risk regulatory pathways and key regulators in coronary artery disease.
Article in Journal of molecular medicine (Berlin, Germany), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Comprehensive analysis of non-coding RNA-mediated endothelial cell-specific regulatory circuits in coronary artery disease risk.Frontiers in genetics · 2025Article
- Network Medicine: A Clinical Approach for Precision Medicine and Personalized Therapy in Coronary Heart Disease.Journal of atherosclerosis and thrombosis · 2020Review
- A network-based analysis for mining the risk pathways in glioblastoma.Oncology letters · 2019Article
- Detecting the long non‑coding RNA signature related to spinal cord ependymal tumor subtype using a genome‑wide methylome analysis approach.Molecular medicine reports · 2019Article
- Systematic Characterization of Circular RNA-Associated CeRNA Network Identified Novel circRNA Biomarkers in Alzheimer's Disease.Frontiers in bioengineering and biotechnology · 2019Article
- MicroRNA regulatory pathway analysis identifies miR-142-5p as a negative regulator of TGF-β pathway via targeting SMAD3.Oncotarget · 2016Article
- Translational Bioinformatics: Past, Present, and Future.Genomics, proteomics & bioinformatics · 2016Review
- miRTarBase 2016: updates to the experimentally validated miRNA-target interactions database.Nucleic acids research · 2016Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
unlabelledCoronary artery disease (CAD) is the most common type of heart disease. However, the molecular mechanisms of CAD remain elusive. Regulatory pathways are known to play crucial roles in many pathogenic processes. Thus, inferring risk regulatory pathways is an important step toward elucidating the mechanisms underlying CAD. With advances in high-throughput data, we developed an integrated systems approach to identify CAD risk regulatory pathways and key regulators. Firstly, a CAD-related core subnetwork was identified from a curated transcription factor (TF) and microRNA (miRNA) regulatory network based on a random walk algorithm. Secondly, candidate risk regulatory pathways were extracted from the subnetwork by applying a breadth-first search (BFS) algorithm. Then, risk regulatory pathways were prioritized based on multiple CAD-associated data sources. Finally, we also proposed a new measure to prioritize upstream regulators. We inferred that phosphatase and tensin homolog (PTEN) may be a key regulator in the dysregulation of risk regulatory pathways. This study takes a closer step than the identification of disease subnetworks or modules. From the risk regulatory pathways, we could understand the flow of regulatory information in the initiation and progression of the disease. Our approach helps to uncover its potential etiology. KEY MESSAGES: We developed an integrated systems approach to identify risk regulatory pathways. We proposed a new measure to prioritize the key regulators in CAD. PTEN may be a key regulator in dysregulation of the risk regulatory pathways.
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