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Gene correlation network analysis to identify regulatory factors in idiopathic pulmonary fibrosis.

ABSTRACT: BACKGROUND:Idiopathic pulmonary fibrosis (IPF) is a severe lung disease characterised by extensive pathological changes. The objective for this study was to identify the gene network and regulators underlying disease pathology in IPF and its association with lung function. METHODS:Lung Tissue Research Consortium dataset with 262 IPF and control subjects (GSE47460) was randomly divided into two non-overlapping groups for cross-validated differential gene expression analysis. Consensus weighted gene coexpression network analysis identified overlapping coexpressed gene modules between both IPF groups. Modules were correlated with lung function (diffusion capacity, DLCO; forced expiratory volume in 1 s, FEV1; forced vital capacity, FVC) and enrichment analyses used to identify biological function and transcription factors. Module correlation with miRNA data (GSE72967) identified associated regulators. Clinical relevance in IPF was assessed in a peripheral blood gene expression dataset (GSE93606) to identify modules related to survival. RESULTS:Correlation network analysis identified 16 modules in IPF. Upregulated modules were associated with cilia, DNA replication and repair, contractile fibres, B-cell and unfolded protein response, and extracellular matrix. Downregulated modules were associated with blood vessels, T-cell and interferon responses, leucocyte activation and degranulation, surfactant metabolism, and cellular metabolic and catabolic processes. Lung function correlated with nine modules (eight with DLCO, five with FVC). Intermodular network of transcription factors and miRNA showed clustering of fibrosis, immune response and contractile modules. The cilia-associated module was able to predict survival (p=0.0097) in an independent peripheral blood IPF cohort. CONCLUSIONS:We identified a correlation gene expression network with associated regulators in IPF that provides novel insight into the pathological process of this disease.

SUBMITTER: McDonough JE

PROVIDER: S-EPMC6467239 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature

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Gene correlation network analysis to identify regulatory factors in idiopathic pulmonary fibrosis.

McDonough John E JE Kaminski Naftali N Thienpont Bernard B Hogg James C JC Vanaudenaerde Bart M BM Wuyts Wim A WA

Thorax 20181026 2

<h4>Background</h4>Idiopathic pulmonary fibrosis (IPF) is a severe lung disease characterised by extensive pathological changes. The objective for this study was to identify the gene network and regulators underlying disease pathology in IPF and its association with lung function.<h4>Methods</h4>Lung Tissue Research Consortium dataset with 262 IPF and control subjects (GSE47460) was randomly divided into two non-overlapping groups for cross-validated differential gene expression analysis. Consen ...[more]

PMID: 30366970

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Project description:BackgroundIdiopathic pulmonary fibrosis (IPF) is a fatal heterogeneous disease with a varied clinical course that is difficult to predict. Accurate predictive models are urgently needed to identify individuals with poor survival for the optimal timing of referral for transplantation and provide some clues for mechanistic research on disease progression.MethodsWe obtained the gene expression profiles of bronchoalveolar lavage fluid (BALF) from the Gene Expression Omnibus. Individuals from the GPL14550 platform were assigned to the derivation cohort (n=112) and individuals from the GPL17077 platform to the validation cohort (n=64). Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were applied to select candidate genes for overall survival. A nomogram model was constructed based on Cox hazard regression analysis. The model was assessed by C-statistic, calibration curve, and decision curve analysis (DCA) and was externally validated.ResultsA nomogram model comprising seven genes was constructed. Excellent discrimination and calibration were observed in the derivation (C-index 0.815) and validation (C-index 0.812) cohorts. The AUCs for predicting 1-, 2- and 3-year survival were 0.857, 0.918, 0.930 in the derivation cohort and 0.850, 0.880, 0.925 in the validation cohort, respectively. DCA confirmed the clinical applicability of the model. A risk score based on the model was an independent prognostic predictor and could divide patients into high- and low-risk groups. The Kaplan-Meier analysis displayed that high-risk patients exhibited significantly poorer survival compared with low-risk patients. Gene Set Enrichment Analysis (GSEA) showed that high-risk patients were primarily enriched in inflammatory hallmarks, and single sample GSEA (ssGSEA) indicated that the high-risk group is closely correlated with the immune process. These lead to increased insight into mechanisms associated with IPF progression that inflammation mediated by immune response might be involved in the disease progression.ConclusionsThe novel BALF seven-gene model performed well in risk stratification and individualized survival prediction for patients with IPF, facilitating personalized management of IPF patients. It deepened the understanding of the role of inflammation in IPF progression, which needs to be further studied.

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Gene correlation network analysis to identify regulatory factors in idiopathic pulmonary fibrosis.

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Gene correlation network analysis to identify regulatory factors in idiopathic pulmonary fibrosis.

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