AN INTEGRATIVE NETWORK BASED ANALYSIS FOR ELUCIDATING SHARED THERAPEUTIC TARGETS IN COPD AND IDIOPATHIC PULMONARY FIBROSIS

Abstract

Idiopathic Pulmonary Fibrosis (IPF) and Chronic Obstructive Pulmonary Disease (COPD) are progressive respiratory disorders characterized by high mortality rates and pose different clinical manifestations—one by tissue destruction and the other by excessive scarring. However, recent studies are suggestive towards some molecular drivers in common. The aim of this study is directed towards deciphering the common molecular pathways and identifying the hub genes which are shared between the two diseases. The present study utilizes an integrative bioinformatics workflow to decode their shared genetic architecture and put forward the common therapeutic targets. The transcriptomic datasets for COPD and IPF were processed to determine differentially expressed genes, thereafter, computational intersection was done using Python and cross-referencing with the Comparative Toxicogenomics Database (CTD) to obtain a set of high confidence common genes. The functional enrichment analysis using Enrichr pointed out the IL-4/IL-13 axis as a major one among the interleukin signalling pathways that had been significantly overrepresented. The human lung co-expression data based protein-protein interaction network built using NetworkAnalyst showed that TRPV4 was the most centrally located node gene. The incorporation of literature elucidates that TRPV4 acts both as a mechanosensor and an inflammation mediator, thus putting interleukin-driven inflammation in the context of fibrotic remodelling. The outcome of our analysis is that TRPV4 is the commonality of the shared pathology and is thus, a potential common therapeutic target. The results of the present study not only underscore a convergent molecular framework for COPD and IPF but also showcase the potential of integrative bioinformatics in revealing actionable targets for complex, multifactorial diseases.