Functional Analysis of Bronchopulmonary Dysplasia-Related Neuropeptides in Preterm Infants and miRNA-Based Diagnostic Model Construction

Abstract

Background. Bronchopulmonary dysplasia (BPD) has a high mortality rate. This study was aimed at identifying and analysing the risk factors associated with BPD using bioinformatic and mechanical analyses and establishing a predictive model to assess the risk of BPD in preterm infants. Methods. We identified differentially expressed RNAs via the intersection of miRNAs between datasets. Online analysis tools were used to predict genes targeted by differentially expressed miRNAs (DEmiRNAs) and to generate and visualise competing endogenous RNA (ceRNA) coexpression networks. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were subsequently performed on the DEmiRNAs. In addition, an intersection analysis was performed on mRNA and neuropeptide-related genes in the ceRNA network. DEmiRNAs associated with BPD and those involved in ceRNA networks were used to establish a diagnostic prediction model. The GSE108604 dataset was used as a validation set to verify the model. Results. A total of 26 DEmiRNAs were identified from the tracheal aspirates (TAs) of patients with BPD and healthy controls. In addition, a total of 1076 DEmRNAs were obtained from the GSE8586 dataset. Functional enrichment analysis of DEmRNAs revealed an abnormal reduction in mitochondrial-related activity and cellular responses to oxidative stress in patients with BPD. The neuropeptide-related genes OPRL1 and NPPA were found to be upregulated in BPD samples. Eventually, hsa-miR-1258, hsa-miR-298, hsa-miR-483-3p, and hsa-miR-769-5p were screened out and used to establish the prediction model. Calibration curves and detrended correspondence analysis (DCA) revealed that the model had good clinical applicability. Conclusions. The prediction model provided a simple method for individualised assessment, early diagnosis, and prevention of BPD risk in preterm infants.