Table2_Identification and immunological characterization of endoplasmic reticulum stress-related molecular subtypes in bronchopulmonary dysplasia based on machine learning.DOCX

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.

引言：支气管肺发育不良（BPD）是一种危及生命的肺部疾病，主要影响早产儿，具有较高的发病率和死亡率。本研究旨在运用可解释机器学习技术，探究内质网应激相关基因（ERSGs）在BPD患者中的参与情况。方法：我们利用GSE32472数据集评估了支气管肺发育不良中内质网应激相关基因和免疫特征的表达谱。通过62份支气管肺发育不良样本，研究基于内质网应激相关基因的分子簇及其相关免疫细胞浸润。利用WGCNA技术鉴定了簇特异性差异表达基因（DEGs）。在将性能与广义线性模型、极端梯度提升、支持向量机（SVM）模型和随机森林模型进行比较后，应用了最佳机器模型。通过校准曲线、Nomogram、决策曲线分析和外部数据集的使用，对预测效率进行了验证。结果：将支气管肺发育不良样本与对照样本进行比较，分析了失调的内质网应激相关基因和激活的免疫反应。在支气管肺发育不良中，确定了与内质网应激相关的两个不同分子簇。免疫细胞浸润分析显示，各个簇之间的免疫力水平存在显著差异。根据残差和均方根误差以及曲线下面积测量，支持向量机模型显示了最大的区分能力。最终，开发了一个包含五个基因的支持向量机模型，并在外部验证数据集上展示了其优异的性能。通过决策曲线、校准曲线和Nomogram分析，进一步确立了其在预测支气管肺发育不良亚型中的有效性。结论：我们开发了一种潜在预测模型，用于评估内质网应激亚型和支气管肺发育不良患者的临床结局风险，并全面揭示了内质网应激与支气管肺发育不良之间的复杂关联。