Integrated Bioinformatic Approach to Identify Potential Biomarkers against Idiopathic Pulmonary Fibrosis

Abstract

AbstractIdiopathic Pulmonary Fibrosis (IPF) is a chronic and progressive lung disease that leads to gradual decline in lung function. The molecular mechanism and risk factors of this disease are still obscure. Poorly understood etiology of this disease is the major obstacle in the identification of potential biomarkers and drug targets. In this study, microarray gene expression data of normal and IPF patient has been utilized for the statistical analysis of differentially expressed genes (DEGs) with a view to identifying potential molecular signatures using network-based system. Then their functional enrichment analysis revealed their predominant involvement in transcription, protein acetylation, extracellular matrix organization, apoptic process, inflammatory response etc. Protein-Protein Interaction (PPI) network revealed (UBC, PTEN, SOS1, PTK2, FGFR1, YAP1, FOXO1, RACK1, BMP4 and CD44) as hub proteins in IPF. Subsequent regulatory network analysis suggested (E2F1, STAT3, PPARG, MEF2A, FOXC1, GATA3, YY1, GATA2, NFKB1, and FOXL1) as the best regulatory transcriptional signatures and (hsa-mir-155-5p, hsa-mir-16-5p, hsa-mir-17-5p, hsa-mir-19a-3p, hsa-mir-192-5p, hsa-mir-92a-3p, hsa-mir-26b-5p, hsa-mir-335-5p, hsa-mir-124-3p, and hsa-let-7b-5p) as the best post-transcriptional signatures. This study represents proteome and RNA signatures of IPF which might be useful to uphold the present efforts in the discovery of potential biomarkers and treatments of this disease.