Abstract

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a life-threatening disease with an unknown etiology. Gene expression microarray data have provided some insights into the molecular mechanisms of IPF. This study aimed to identify key genes and significant signaling pathways involved in IPF using bioinformatics analysis.

MATERIAL AND METHODS: Differentially expressed genes (DEGs) were identified using integrated analysis of gene expression data with a robust rank aggregation (RRA) method. The Connectivity Map (CMAP) was used to identify gene-expression signatures associated with IPF. Weighted gene coexpression network analysis (WGCNA) was used to explore the functional modules involved in the pathogenesis of IPF.

RESULTS: A total of 191 patients with IPF and 101 normal controls from six genome-wide expression datasets were included. CMAP predicted several small molecular agents as potential gene targets in IPF. Several functional modules were detected that showed the highest correlation with IPF, including an extracellular matrix (ECM) component, and a myeloid leukocyte migration and activation component involved in the immune response. Hub genes were identified in the key functional modules that might have a role in the progression of IPF.

CONCLUSIONS: WGCNA was used to identify functional modules and hub genes involved in the pathogenesis of IPF.

Keywords: Biomarkers, Pharmacological, Gene Regulatory Networks, idiopathic pulmonary fibrosis, biomarkers, Computational Biology, Databases, Genetic, Gene Expression Profiling, Gene Expression Regulation, Lung, Oligonucleotide Array Sequence Analysis, transcriptome