Bioinformatic analysis of the molecular mechanism underlying bronchial pulmonary dysplasia using a text mining approach

Bronchopulmonary dysplasia (BPD) is a common disease of premature infants with very low birth weight. The mechanism is inconclusive. The aim of this study is to systematically explore BPD-related genes and characterize their functions. Natural language processing analysis was used to identify BPD-related genes. Gene data were extracted from PubMed database. Gene ontology, pathway, and network analysis were carried out, and the result was integrated with corresponding database. In this study, 216 genes were identified as BPD-related genes with P< .05, and 30 pathways were identified as significant. A network of BPD-related genes was also constructed with 17 hub genes identified. In particular, phosphatidyl inosito1-3-enzymeserine/threonine kinase signaling pathway invoked the largest number of genes. Insulin was found to be a promising candidate gene related with BPD, suggesting that it may serve as an effective therapeutic target. Our data may help to better understand the molecular mechanisms underlying BPD. However, the mechanisms of BPD are elusive, and further studies are needed.