Pyroptosis plays a crucial role in bronchopulmonary dysplasia (BPD) and is associated with various lung injury illnesses. However, the function of pyroptosis-related genes (PRGs) in BPD remains poorly understood. The gene expression omnibus (GEO) database was searched for information on genes associated with BPD. Twenty-five BPD-related DE-PRGs were identified, all of which were closely associated with pyroptosis regulation and immunological response. LASSO and SVM-RFE algorithms identified CHMP7, NLRC4, NLRP2, NLRP6, and NLRP9 among the 25 differentially expressed PRGs as marker genes with acceptable diagnostic capabilities. Using these five genes, we also generated a nomogram with excellent predictive power. Annotation enrichment analyses revealed that these five genes may be implicated in BPD and numerous BPD-related pathways. In addition, the ceRNA network showed an intricate regulatory link based on the marker genes. In addition, CIBERSORT-based studies revealed that alterations in the immunological microenvironment of BPD patients may be associated with the marker genes. We constructed a diagnostic nomogram and gave insight into the mechanism of BPD. Its diagnostic value for BPD must be evaluated in further research before it can be used in clinical practice.
Introduction: Bronchopulmonary dysplasia (BPD) is a life-threatening lung illness that affects premature infants and has a high incidence and mortality. Using interpretable machine learning, we aimed to investigate the involvement of endoplasmic reticulum (ER) stress-related genes (ERSGs) in BPD patients.Methods: We evaluated the expression profiles of endoplasmic reticulum stress-related genes and immune features in bronchopulmonary dysplasia using the GSE32472 dataset. The endoplasmic reticulum stress-related gene-based molecular clusters and associated immune cell infiltration were studied using 62 bronchopulmonary dysplasia samples. Cluster-specific differentially expressed genes (DEGs) were identified utilizing the WGCNA technique. The optimum machine model was applied after comparing its performance with that of the generalized linear model, the extreme Gradient Boosting, the support vector machine (SVM) model, and the random forest model. Validation of the prediction efficiency was done by the use of a calibration curve, nomogram, decision curve analysis, and an external data set.Results: The bronchopulmonary dysplasia samples were compared to the control samples, and the dysregulated endoplasmic reticulum stress-related genes and activated immunological responses were analyzed. In bronchopulmonary dysplasia, two distinct molecular clusters associated with endoplasmic reticulum stress were identified. The analysis of immune cell infiltration indicated a considerable difference in levels of immunity between the various clusters. As measured by residual and root mean square error, as well as the area under the curve, the support vector machine machine model showed the greatest discriminative capacity. In the end, an support vector machine model integrating five genes was developed, and its performance was shown to be excellent on an external validation dataset. The effectiveness in predicting bronchopulmonary dysplasia subtypes was further established by decision curves, calibration curves, and nomogram analyses.Conclusion: We developed a potential prediction model to assess the risk of endoplasmic reticulum stress subtypes and the clinical outcomes of bronchopulmonary dysplasia patients, and our work comprehensively revealed the complex association between endoplasmic reticulum stress and bronchopulmonary dysplasia.
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