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Development of a blood proteins-based model for bronchopulmonary dysplasia prediction in premature infants
Wanting Ou1, KeJing Lei1, Huanhuan Wang1
1Department of Pediatrics, Dazhou Central Hospital, Dazhou, Sichuan, China.
Insights
This study identifies key blood proteins that can predict bronchopulmonary dysplasia (BPD) in premature infants. This protein-based model offers a promising tool for early BPD detection and potential therapeutic targets.
Area of Science:
- Neonatal Medicine
- Pulmonary Medicine
- Biomarker Discovery
Background:
- Bronchopulmonary dysplasia (BPD) is a prevalent chronic lung disease in premature infants.
- Early identification of BPD risk factors is crucial for timely intervention.
- Blood proteins are potential early indicators for BPD development.
Purpose of the Study:
- To develop a predictive model for BPD using blood protein expression profiles.
- To identify specific proteins associated with BPD development in premature infants.
- To explore potential therapeutic targets for BPD.
Main Methods:
- Utilized Gene Expression Omnibus (GEO) data (GSE121097) including protein expression and clinical data.
- Applied Weighted Gene Co-expression Network Analysis (WGCNA) and differential protein analysis.
- Developed a BPD prediction model using Least Absolute Shrinkage and Selection Operator (LASSO) regression.
Main Results:
- Identified significant protein modules (black, magenta, turquoise) correlated with BPD.
- Found 59 overlapping proteins enriched in 253 Gene Ontology (GO) terms and 11 KEGG pathways.
- LASSO analysis reduced these to 8 proteins, achieving high predictive performance (AUC 1.00 training, 0.96 testing).
Conclusions:
- Established a reliable blood-protein based model for early BPD prediction in premature infants.
- The identified proteins may offer insights into BPD pathogenesis.
- This model could aid in reducing the burden and severity of BPD.
Background:
Bronchopulmonary dysplasia (BPD) is the most common chronic pulmonary disease in premature infants. Blood proteins may be early predictors of the development of this disease.
Methods:
In this study, protein expression profiles (blood samples during their first week of life) and clinical data of the GSE121097 was downloaded from the Gene Expression Omnibus. Weighted gene co-expression network analysis (WGCNA) and differential protein analysis were carried out for variable dimensionality reduction and feature selection. Least absolute shrinkage and selection operator (LASSO) were conducted for BPD prediction model development. The performance of the model was evaluated by the receiver operating characteristic (ROC) curve, calibration curve, and decision curve.
Results:
The results showed that black module, magenta module and turquoise module, which included 270 proteins, were significantly correlated with the occurrence of BPD. 59 proteins overlapped between differential analysis results and above three modules. These proteins were significantly enriched in 253 GO terms and 11 KEGG signaling pathways. Then, 59 proteins were reduced to 8 proteins by LASSO analysis in the training cohort. The proteins model showed good BPD predictive performance, with an AUC of 1.00 (95% CI 0.99-1.00) and 0.96 (95% CI 0.90-1.00) in training cohort and test cohort, respectively.
Conclusion:
Our study established a reliable blood-protein based model for early prediction of BPD in premature infants. This may help elucidate pathways to target in lessening the burden or severity of BPD.
