Uncovering The Underlying Mechanism Of Bronchopulmonary Dysplasia Through Bioinformatics Analysis And Text Mining

?Background?Bronchopulmonary dysplasia(BPD)is the common chronic complication of respiratory system in premature infants,characterized by stagnation of alveolar development and damage to pulmonary blood vessels.Premature delivery,prenatal or postpartum infection,mechanical ventilation,oxidative stress injury,and patent ductus arteriosus are all closely related to the occurrence and development of BPD.The reasons for the occurrence and promotion of BPD are complex.Various biological processes such as prenatal and postpartum inflammation,abnormal angiogenesis,and alveolar obstruction are involved,involving multiple genes such as TGF-?,VEGF,and FGF-10.The mechanism of expression change has not yet been fully elucidated.Therefore,studying the changes of specific gene expression during the pathogenesis of BPD is essential for elucidating the pathogenesis of BPD and screening biomarkers.?Aim?1.Screen the differentially expressed genes between the BPD group and the control group in the GSE25286 data set.2.Explore the enriched functional signaling pathways of DEGs.3.Use DEGs to construct a protein-protein interaction network and screen key modules and hub genes in the network,and verify the expression of hub genes.4.Explore the functional pathways of key modules and hub gene enrichment.5.Text mining the NCBI database to explore potential related genes in the BPD.?Materials and methods?We downloaded the data set GSE25286 from the GEO database,used the online analysis tool GEO2 R in the GEO database to identify DEGs between hyperoxia-exposed lung tissue and the control group.Web Gestalt interactive analysis tool was used to perform GO,KEGG,and Reactome enrichment analysis on DEGs.The PPI network was constructed using the STRING database,and the PPI network was visualized using cytoscape software.The MCODE plug-in was used to identify key modules in the PPI network,and the MCC algorithm is used to screen hub genesfrom the key modules and verify the expression of hub genes in multiple BPD-related data sets(GSE32472,GSE51039,GSE121097).Pubmed2 ensembl online interactive analysis tool was used to mine and count the BPD-related genes in the NCBI database,and analyze their enriched functional pathways.?Results?A total of 1289 DEGs were identified between BPD and the control group,including 568 down-regulated genes and 721 up-regulated genes.A key module was selected from the PPI network.The enrichment analysis showed that the module was significantly enriched in leukocyte migration,tissue remodeling,myocyte proliferation,cell chemotaxis,angiogenesis,sphingolipid signaling pathway,IL-17 signaling pathways,tumor necrosis factor signaling pathways and many other pathways.A total of 8 hub genes were screened out.Among them,the expression of IL6 in the peripheral blood data of children with BPD was significantly increased,and increased with the severity of BPD.The results of text mining gene set enrichment analysis are consistent with DEGs enrichment analysis results.?Conelusion?This study explored the differences in gene expression between BPD lung tissue and normal lung tissue through bioinformatics analysis.The enrichment analysis results showed that inflammation and angiogenesis have a key role in the development of BPD,and the selected hub genes may play a key role in BPD,and have the potential to become an early BPD biomarker.This study provides novel insights into the molecular mechanism of BPD and new ideas for screening possible therapeutic targets.