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01.12.2015 | Research article | Ausgabe 1/2015 Open Access

BMC Pulmonary Medicine 1/2015

A functional genomic model for predicting prognosis in idiopathic pulmonary fibrosis

BMC Pulmonary Medicine > Ausgabe 1/2015
Yong Huang, Shwu-Fan Ma, Rekha Vij, Justin M. Oldham, Jose Herazo-Maya, Steven M. Broderick, Mary E. Strek, Steven R. White, D. Kyle Hogarth, Nathan K. Sandbo, Yves A. Lussier, Kevin F. Gibson, Naftali Kaminski, Joe G. N. Garcia, Imre Noth
Wichtige Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s12890-015-0142-8) contains supplementary material, which is available to authorized users.
Yong Huang, Shwu-Fan Ma and Rekha Vij contributed equally to this work.

Competing interests

The authors declare that they have no competing interests.

Authors’ contributions

All authors contributed to the conception and design of the study; YH and JO performed the statistical analysis; YH drafted the manuscript; SFM, RV, JO, YAL, JGN, IN contributed to the writing of the manuscript; YH, SFM, RV contributed to this work equally; SMB, MSW collected biological samples; SFM supervised the sample collection; DKH, IN, NKS, MES, RV, SRW collected phenotypic data which were further evaluated by IN and RV; All authors read and approved the final manuscript.



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.


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 < 0.05); 2) Differentially expressed between observed “good” vs. “poor” prognosis with fold change (FC) >1.5 and false discovery rate (FDR) < 2 %; and 3) Predictive of mortality (p < 0.05) in univariate Cox regression analysis. “Survival risk group prediction” was adopted to construct a functional genomic model that used the IPF prognostic predictor gene set to derive a prognostic index (PI) for each patient into either high or low risk for survival outcomes. Prediction accuracy was assessed with a repeated 10-fold cross-validation algorithm and independently assessed in two validation cohorts through multivariate Cox regression survival analysis.


A set of 118 IPF prognostic predictor genes was used to derive the functional genomic model and PI. In the training cohort, high-risk IPF patients predicted by PI had significantly shorter survival compared to those labeled as low-risk patients (log rank p < 0.001). The prediction accuracy was further validated in two independent cohorts (log rank p < 0.001 and 0.002). Functional pathway analysis revealed that the canonical pathways enriched with the IPF prognostic predictor gene set were involved in T-cell biology, including iCOS, T-cell receptor, and CD28 signaling.


Using supervised and unsupervised analyses, we identified a set of IPF prognostic predictor genes and derived a functional genomic model that predicted high and low-risk IPF patients with high accuracy. This genomic model may complement current prognostic tools to deliver more personalized care for IPF patients.
Additional file 1: Additional Methods and Tables. (DOCX 103 kb)
Additional file 2: Figure S1. Detection of gene co-expression modules in training cohort. Gene expression intensities obtained from Exon 1.0 ST Array were normalized. Probe sets were mapped to U133 plus 2.0 Array and filtered as described in Additional file 1. A total of 2,718 unique genes were retained and subjected to R package “Weighted Gene Co-expression Network Analysis (WGCNA)” to identify co-expressed gene modules. A). Optimization and selection of power for adjacency transition of gene-gene correlation matrix (power =7). B). Cluster dendrogram of the gene co-expression modules represented by different colors. Seven gene co-expression modules were detected by hierarchical clustering using dynamic tree cut algorithm integrated in WGCNA with the following parameters: power=7, minModuleSize=120, mergeCutHeight= 0.3. Unclustered genes (genes not correlated with other genes) were collected in Grey module. (PPTX 187 kb)
Additional file 3: Figure S2. Gene interaction network of IPF prognostic predictor genes. Significant gene interaction networks were determined using Ingenuity Pathway Analysis (IPA) software. Node shapes denoting different functions were depicted in right panel box. Green and red denote down and up-regulated genes, respectively. (PPTX 544 kb)
Additional file 4: Figure S3. Concordance of IPF prognostic predictor genes between training and each validation cohort. The fold change of each gene between predicted low-risk and high-risk prognosis patients was plotted between training (X-axis) and validation cohort (Y-axis). (PPTX 51 kb)
Additional file 5: Figure S4. Receiver-Operating-Characteristic (ROC) analysis of genomic model for diagnosis prediction. ROC curves of UCV cohort consisting of IPF patients and healthy individuals were plotted based on the Prognostic Index (PI) derived from IPF genomic model. AUC (Area-Under-Curve) is displayed in the graph. The red line denotes 10 % false alarm (1-Specificity). (PPTX 79 kb)
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