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Identification and immunological characterization of endoplasmic reticulum stress-related molecular subtypes in
Ziyu Tao1, Yan Mao2, Yifang Hu3
1Department of Ultrasound, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.
Insights
This study identifies two distinct molecular subtypes of bronchopulmonary dysplasia (BPD) linked to endoplasmic reticulum (ER) stress. A novel machine learning model accurately predicts BPD risk and outcomes based on ER stress gene expression.
Area of Science:
- Genomics
- Computational Biology
- Neonatology
Background:
- Bronchopulmonary dysplasia (BPD) is a significant cause of mortality in premature infants.
- Endoplasmic reticulum (ER) stress is implicated in BPD pathogenesis.
- Understanding ER stress-related genes (ERSGs) is crucial for BPD management.
Purpose of the Study:
- To investigate the role of ERSGs in BPD using interpretable machine learning.
- To identify molecular subtypes of BPD based on ER stress.
- To develop a predictive model for BPD risk and clinical outcomes.
Main Methods:
- Analysis of ERSGs and immune features in BPD using the GSE32472 dataset.
- Clustering of BPD samples based on ER stress gene expression.
- Differential gene expression analysis using Weighted Gene Co-expression Network Analysis (WGCNA).
- Comparison and validation of machine learning models, including Support Vector Machine (SVM).
Main Results:
- Two distinct molecular clusters associated with ER stress in BPD were identified.
- Significant differences in immune cell infiltration were observed between clusters.
- An SVM model integrating five ERSGs demonstrated excellent predictive performance for BPD subtypes.
- Validation confirmed the model's accuracy and clinical utility.
Conclusions:
- A novel, validated prediction model for ER stress-related BPD subtypes and clinical outcomes was developed.
- The study highlights the complex interplay between ER stress and BPD.
- This research offers potential for improved risk assessment and personalized treatment strategies for BPD.
Abstract:
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.
