Computational Drug Discovery in Diaphragm dysfunction via Text Mining and Biomedical Database

Abstract

The diaphragm, which is crucial for ventilation, is the primary muscle responsible for inspiration. Patients with severe burns who experience diaphragmatic dysfunction have an increased risk of mortality. Unfortunately, there are currently no effective medications available to prevent or treat this condition. The objective of our study is to utilize bioinformatics to identify potential genes and drugs associated with diaphragmatic dysfunction. In this study, text mining techniques were utilized to identify genes associated with diaphragmatic dysfunction and recovery. Common genes were then analyzed using GO and KEGG pathway analysis, as well as protein-protein interaction (PPI) network analysis. The obtained hub genes were processed using Cytoscape software, and their expression levels in diaphragmatic dysfunction were validated using quantitative real-time polymerase chain reaction (qRT-PCR) in severe burn rats. Genes that were confirmed were then examined in drug-gene interaction databases to identify potential drugs associated with these genes. Our analysis revealed 96 genes that were common to both the "Diaphragm dysfunction" and "Functional Recovery" text mining concepts. Gene enrichment analysis identified 19 genes representing ten pathways. qRT-PCR showed a significant increase in expression levels of 13 genes, including CCL2, CCL3, CD4, EGF, HGF, IFNG, IGF1, IL17A, IL6, LEP, PTGS2, TGFB1, and TNF, in samples with diaphragmatic dysfunction. Additionally, we found that a total of 56 drugs targeted 5 potential genes. These findings provide new insights into the development of more effective drugs for treating diaphragmatic dysfunction, and also present substantial opportunities for researching new target pharmacology and promoting drugs in the pharmaceutical industry.

Keywords: data mining; diaphragm dysfunction; drug discovery; functional recovery; severe burns.