Development and validation of the prediction model based on autophagy-associated genes in bronchopulmonary dysplasia

Background: Bronchopulmonary dysplasia (BPD) is the most common chronic respiratory disease among preterm infants. Owing to the limitations in current diagnostic methods, developing a predictive model for BPD is crucial. Methods: Using 243 autophagy-associated genes and dataset GSE32472, differential expression of autophagy-associated genes was identified at postnatal days 5, 14, and 28 between BPD patients and controls. LASSO and multivariate logistic regression analyses were performed to screen for diagnostic prediction genes. Receiver Operating Characteristic, Harrell's concordance index, and decision curve analysis (DCA) were used to evaluate the diagnostic prediction model in GSE32472 and GSE220135. A BPD mouse model was constructed and qRT-PCR and Western blot were used to verify gene expression in lung tissue. Results: Based on p < 0.05, we constructed a diagnostic prediction model for BPD using WIPI1, TOMM70A, BAG3, and PRKCQ. For the training database, the model's C-index and Area under Curve were both 0.941, and a high applicability value was demonstrated by the DCA curve. These outcomes were also confirmed in the validation cohort GSE220135, demonstrating the superior diagnostic prediction capability of our approach. In addition, significant variations in immune cell infiltration were observed between BPD patients and controls. According to the results of qRT-PCR, BPD model mice had significantly lower expression levels of WIPI1, TOMM70A, BAG3, and PRKCQ than controls. Conclusions: We constructed and validated a diagnostic prediction model for BPD based on WIPI1, TOMM70A, BAG3, and PRKCQ. These four genes may influence BPD development by regulating immune responses and immune cells.