ArticleBMC medical genomics2011
Information encoded in a network of inflammation proteins predicts clinical outcome after myocardial infarction.
Article in BMC medical genomics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Neutrophil-to-lymphocyte ratio mediates pulmonary consolidation through oxidative stress in children withAnnals of medicine · 2026Article
- Network medicine in Cardiovascular Research.Cardiovascular research · 2021Review
- Peripheral Blood RNAs and Left Ventricular Dysfunction after Myocardial Infarction: Towards Translation into Clinical Practice.Journal of cardiovascular translational research · 2021Review
- A developmental lineage-based gene co-expression network for mouse pancreatic β-cells reveals a role forDevelopment (Cambridge, England) · 2021Article
- Network Medicine in Pathobiology.The American journal of pathology · 2019Review
- Systems biology: An emerging strategy for discovering novel pathogenetic mechanisms that promote cardiovascular disease.Global cardiology science & practice · 2016Review
- Article
- Pathway mapping and development of disease-specific biomarkers: protein-based network biomarkers.Journal of cellular and molecular medicine · 2015Review
- miRNome in myocardial infarction: Future directions and perspective.World journal of cardiology · 2014Review
- Selecting biologically informative genes in co-expression networks with a centrality score.Biology direct · 2014Article
- Deciphering the molecular basis of human cardiovascular disease through network biology.Current opinion in cardiology · 2012Review
- Systems pathology: a critical review.Molecular oncology · 2012Review
- Role of clinical bioinformatics in the development of network-based Biomarkers.Journal of clinical bioinformatics · 2011Article
- Using systems biology approaches to understand cardiac inflammation and extracellular matrix remodeling in the setting of myocardial infarction.Wiley interdisciplinary reviews. Systems biology and medicineArticle
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInflammation plays an important role in cardiac repair after myocardial infarction (MI). Nevertheless, the systems-level characterization of inflammation proteins in MI remains incomplete. There is a need to demonstrate the potential value of molecular network-based approaches to translational research. We investigated the interplay of inflammation proteins and assessed network-derived knowledge to support clinical decisions after MI. The main focus is the prediction of clinical outcome after MI.
methodsWe assembled My-Inflamome, a network of protein interactions related to inflammation and prognosis in MI. We established associations between network properties, disease biology and capacity to distinguish between prognostic categories. The latter was tested with classification models built on blood-derived microarray data from post-MI patients with different outcomes. This was followed by experimental verification of significant associations.
resultsMy-Inflamome is organized into modules highly specialized in different biological processes relevant to heart repair. Highly connected proteins also tend to be high-traffic components. Such bottlenecks together with genes extracted from the modules provided the basis for novel prognostic models, which could not have been uncovered by standard analyses. Modules with significant involvement in transcriptional regulation are targeted by a small set of microRNAs. We suggest a new panel of gene expression biomarkers (TRAF2, SHKBP1 and UBC) with high discriminatory capability. Follow-up validations reported promising outcomes and motivate future research.
conclusionThis study enhances understanding of the interaction network that executes inflammatory responses in human MI. Network-encoded information can be translated into knowledge with potential prognostic application. Independent evaluations are required to further estimate the clinical relevance of the new prognostic genes.
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