A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis

  • Yong Huang (Creator)
  • Shwu Fan Ma (Contributor)
  • Rekha Vij (Creator)
  • Justin M. Oldham (Creator)
  • Jose D. Herazo-Maya (Creator)
  • Steven M. Broderick (Creator)
  • Mary Strek (Creator)
  • Steven R. White (Creator)
  • Kyle Hogarth (Contributor)
  • Nathan K. Sandbo (Creator)
  • Yves A. Lussier (University of Chicago) (Creator)
  • Kevin F. Gibson (Creator)
  • Naftali Kaminski (Creator)
  • Joe GN Garcia (Creator)
  • Imre Noth (Creator)

Dataset

Description

Abstract Background The course of disease for patients with idiopathic pulmonary fibrosis (IPF) is highly heterogeneous. Prognostic models rely on demographic and clinical characteristics and are not reproducible. Integrating data from genomic analyses may identify novel prognostic models and provide mechanistic insights into IPF. Methods Total RNA of peripheral blood mononuclear cells was subjected to microarray profiling in a training (45 IPF individuals) and two independent validation cohorts (21 IPF/10 controls, and 75 IPF individuals, respectively). To identify a gene set predictive of IPF prognosis, we incorporated genomic, clinical, and outcome data from the training cohort. Predictor genes were selected if all the following criteria were met: 1) Present in a gene co-expression module from Weighted Gene Co-expression Network Analysis (WGCNA) that correlated with pulmonary function (p 1.5 and false discovery rate (FDR) 
Date made available2015
Publisherfigshare

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