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Development and validation of the prediction model based on autophagy-associated genes in bronchopulmonary dysplasia
Qingqing Liu1,2, Meiyu Zhang3, Qingqing Xiang1,2
1Department of Pediatrics, Women and Children's Hospital of Chongqing Medical University, Chongqing, China.
Insights
A new diagnostic model using four genes (WIPI1, TOMM70A, BAG3, PRKCQ) can predict bronchopulmonary dysplasia (BPD) in preterm infants. This model shows high accuracy and may help identify infants at risk for BPD.
Area of Science:
- Biomedical research
- Genomics
- Neonatal medicine
Background:
- Bronchopulmonary dysplasia (BPD) is a common chronic respiratory disease in preterm infants.
- Current diagnostic methods for BPD have limitations.
- Developing a predictive model for BPD is crucial for early intervention.
Purpose of the Study:
- To develop and validate a predictive model for BPD using autophagy-associated genes.
- To identify key genes for accurate BPD diagnosis.
- To investigate the role of these genes in BPD pathogenesis.
Main Methods:
- Analysis of autophagy-associated genes in BPD patients and controls using dataset GSE32472.
- Application of LASSO and logistic regression for gene selection.
- Validation of the predictive model using datasets GSE32472 and GSE220135.
- Construction of a BPD mouse model for gene expression verification.
Main Results:
- A diagnostic prediction model for BPD was constructed using WIPI1, TOMM70A, BAG3, and PRKCQ.
- The model demonstrated high diagnostic accuracy with a C-index and AUC of 0.941 in the training set.
- Model performance was validated in an independent cohort (GSE220135).
- Significant differences in immune cell infiltration and gene expression were observed in BPD patients and the mouse model.
Conclusions:
- A validated diagnostic prediction model for BPD was developed using four genes: WIPI1, TOMM70A, BAG3, and PRKCQ.
- These genes may play a role in BPD development through the regulation of immune responses.
- The findings support the potential of this gene panel for early BPD detection and management.
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.
