Comprehensive analysis of characteristic genes of inflammation-related bronchopulmonary dysplasia based on bioinformatics methods

Abstract

Background: Bronchopulmonary dysplasia (BPD) is the most common complication of premature infants, and inflammation plays an important role in it. In this research, the key genes of inflammation-related BPD were comprehensively analyzed by bioinformatics methods, and a prediction model was constructed. Methods: We obtained the GSE188944 and GSE108794 datasets from the GEO database and conducted GO and KEGG enrichment analyses to identify differential genes associated with inflammation-related BPD. Through the implementation of LASSO regression, RF, and XGBOOST algorithms, identify the key genes. The accuracy of these key genes in predicting BPD was assessed using ROC curve analysis and AUC calculations. In addition, the GSE190215 data set is used for external verification. Furthermore, we performed GSEA to quantify the key genes and analyze pathways, examine immune cell infiltration in BPD tissues, and explore the correlation amongst the key genes. Additionally, we used relevant databases to predict the miRNA and transcription factors associated with the key genes. Results: This study successfully identified seven key genes (HLA-DRB1, SLC39A8, IL2RA, SYK, CD180, IL6ST, IL18R1) as novel markers for constructing a diagnostic prediction model for BPD. GSEA analysis revealed enrichment pathways related to the key genes, and significant differences in the infiltration of CD8+ T cells, natural killer cells, and mast cells were observed between BPD and non-BPD samples. Conclusion: We successfully developed a risk model for inflammation-related BPD key genes, displaying favorable verification performance, but poor external verification performance. These findings suggest that inflammatory genes may influence the occurrence and progression of BPD through immune cell infiltration.