Bronchopulmonary dysplasia: a crime of opportunity?

Identification and validation of cuproptosis related genes and signature markers in bronchopulmonary dysplasia disease using bioinformatics analysis and machine learning

BACKGROUND

Bronchopulmonary Dysplasia (BPD) has a high incidence and affects the health of preterm infants. Cuproptosis is a novel form of cell death, but its mechanism of action in the disease is not yet clear. Machine learning, the latest tool for the analysis of biological samples, is still relatively rarely used for in-depth analysis and prediction of diseases.

METHODS AND RESULTS

First, the differential expression of cuproptosis-related genes (CRGs) in the GSE108754 dataset was extracted and the heat map showed that the expression of NFE2L2 gene was significantly higher in the control group whereas the expression of GLS gene was significantly higher in the treatment group. Chromosome location analysis showed that both the genes were positively correlated and associated with chromosome 2. The results of immune infiltration and immune cell differential analysis showed differences in the four immune cells, significantly in Monocytes cells. Five new pathways were analyzed through two subgroups based on consistent clustering of CRG expression. Weighted correlation network analysis (WGCNA) set the screening condition to the top 25% to obtain the disease signature genes. Four machine learning algorithms: Generalized Linear Models (GLM), Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGB) were used to screen the disease signature genes, and the final five marker genes for disease prediction. The models constructed by GLM method were proved to be more accurate in the validation of two datasets, GSE190215 and GSE188944.

CONCLUSION

We eventually identified two copper death-associated genes, NFE2L2 and GLS. A machine learning model-GLM was constructed to predict the prevalence of BPD disease, and five disease signature genes NFATC3, ERMN, PLA2G4A, MTMR9LP and LOC440700 were identified. These genes that were bioinformatics analyzed could be potential targets for identifying BPD disease and treatment.

Comparisons of two definitions of bronchopulmonary dysplasia for the premature infants

OBJECTIVE

Bronchopulmonary dysplasia (BPD) is a very common respiratory disease in premature infants, but there is still a different understanding of the definition of BPD. Therefore, this study is intended to compare are main clinical results and health economic expenditures under different BPD definitions.

METHODS

This study included premature infants who came into the neonatal intensive care unit (NICU) from January 2018 to January 2020, who were not more than 32 weeks of premenstrual age. The main clinical consequences and health economic expenditures were analyzed by the National Institute of Health and Human Development definition (Workshop) and Jensen definition.

RESULTS

Total 303 survivors were evaluated at 36 weeks. BPD was performed in 47.5% and 37.6% of infants, respectively, with Workshop's and Jensen's definitions. The percentage of unclassified BPD infants was 0.9% in Workshop's and 3.3% in Jensen's definitions. Further discussing the health economic burden and found that Jensen's definitions had a significantly correlated with NICU charges than the Workshop's definitions.

CONCLUSION

Comparing the Workshop definition of BPD, the Jensens definition was better correlated to health expenditure.