In-silico identification and experimental validation of shared genes and pathways to decipher the molecular links between COPD and MASLD

Abstract

Chronic obstructive pulmonary disease (COPD) and Metabolic dysfunction-associated steatotic liver disease (MASLD) are complex, heterogenous diseases caused by genetic, lifestyle, and environmental factors, with systemic inflammation and redox imbalance playing a significant role in the pathogenesis of both diseases. However, a common mechanism that correlates the two diseases remains unclear. Here, we have used a bioinformatics approach to understand the molecular network and pathway to predict potentially common genes that can be targeted to prevent these conditions. This study used three online databases and clinical datasets on Gene Expression Omnibus. PPI network analyses, gene-ontology, and pathway analysis were done utilizing advanced bioinformatics tools such as Enrichr, String, and Cytoscape. Furthermore, candidate drug prediction was also performed using DSigDB. Bioinformatic analysis of databases and datasets identified twelve common genes (CXCL8, MMP9, IL1β, ITGB2, SPP1, PTGS2, SOCS3, BAX, GDF15, S100A8, CCL2, and MYC) in COPD and MASLD. It revealed shared signaling pathways and gene ontology (biological processes, cellular components, and molecular functions) in both diseases. Five hub genes (CCL2, CCL5, CXCL8, CXCL10, and CXCL1) were also identified, which play a significant role in COPD and MASLD. The study also predicted potential drugs against the common differentially expressed proteins using DSigDB. We performed RT-qPCR to validate the differential expression of the common genes in COPD and MASLD models, which confirmed the in-silico data. Furthermore, we investigated the effect of one of the predicted drug molecules, NS-398, a selective COX-2 inhibitor, in the COPD and MASLD models, which showed significant inhibition of expression of many upregulated genes, highlighting its potential therapeutic impact. To conclude, data obtained from this study showed key common and hub genes that may be involved in COPD and MASLD pathophysiology and highlight the potential mechanisms underlying their activation.

Keywords: Bioinformatics; COPD; Drug target prediction; MASLD; Protein-protein interaction; Shared genes.