Network analysis of transcriptomics data for the prediction and prioritization of membrane-associated biomarkers for idiopathic pulmonary fibrosis (IPF) by bioinformatics approach.

Abstract

Idiopathic pulmonary fibrosis (IPF) is a rare yet crucial persistent lung disorder that actuates scarring of lung tissues, which makes breathing difficult. Smoking, environmental pollution, and certain viral infections could initiate lung scarring. However, the molecular mechanism involved in IPF remains elusive. To develop an efficient therapeutic arsenal against IPF, it is vital to understand the pathology and deviations in biochemical pathways that lead to disorder. In this study, we availed network analysis and other computational pipelines to delineate the prominent membrane proteins as diagnostic biomarkers and therapeutic targets for IPF. This study yielded a significant role of glycosaminoglycan binding, endothelin, and GABA-B receptor signaling pathway in IPF pathogenesis. Furthermore, ADCY8, CRH, FGB, GPR17, MCHR1, NMUR1, and SAA1 genes were found to be immensely involved with IPF, and the enrichment pathway analysis suggests that most of the pathways were corresponding to membrane transport and signal transduction functionalities. This analysis could help in better understanding the molecular mechanism behind IPF to develop an efficient therapeutic target or biomarkers for IPF.