title: Information encoded in a network of inflammation proteins predicts clinical outcome after myocardial infarction.
abstract: BACKGROUND
  Inflammation 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.
  
  
  METHODS
  We 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.
  
  
  RESULTS
  My-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.
  
  
  CONCLUSIONS
  This 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.
authors.affiliation: Laboratory of Cardiovascular Research, Public Research Centre for Health, CRP-Santé, L-1150, Luxembourg. Francisco.Azuaje@crp-sante.lu
authors.forename: Francisco J
authors.initials: FJ
authors.lastname: Azuaje
authors.forename: Sophie
authors.initials: S
authors.lastname: Rodius
authors.forename: Lu
authors.initials: L
authors.lastname: Zhang
authors.forename: Yvan
authors.initials: Y
authors.lastname: Devaux
authors.forename: Daniel R
authors.initials: DR
authors.lastname: Wagner
mesh_terms: D000951:Antigens, Neoplasm / Q000235:genetics / Q000378:metabolism; D015415:Biomarkers / Q000378:metabolism; D005500:Follow-Up Studies; D006801:Humans; D007249:Inflammation / Q000235:genetics / Q000378:metabolism; D018836:Inflammation Mediators / Q000378:metabolism*; D008875:Middle Aged; D009203:Myocardial Infarction / Q000175:diagnosis / Q000235:genetics / Q000378:metabolism*; D011379:Prognosis; D047992:TNF Receptor-Associated Factor 2 / Q000235:genetics / Q000378:metabolism
chemical_list: D000951:Antigens, Neoplasm; D015415:Biomarkers; D018836:Inflammation Mediators; D047992:TNF Receptor-Associated Factor 2; C440529:UBC antigen