Tao Ziyu, Mao Yan, Hu Yifang, Tang Xinfang, Wang Jimei, Zeng Ni, Bao Yunlei, Luo Fei, Wu Chuyan, Jiang Feng
Department of Ultrasound, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China.
Department of Pediatrics, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Front Physiol. 2023 Jan 9;13:1084650. doi: 10.3389/fphys.2022.1084650. eCollection 2022.
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. 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. 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. 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.
支气管肺发育不良（BPD）是一种危及生命的肺部疾病，影响早产儿，发病率和死亡率都很高。我们旨在通过可解释的机器学习方法，研究内质网（ER）应激相关基因（ERSGs）在BPD患者中的作用。我们使用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的表达谱和免疫特征。利用62份支气管肺发育不良样本，研究了基于内质网应激相关基因的分子簇及相关免疫细胞浸润情况。运用WGCNA技术识别特定簇的差异表达基因（DEGs）。在将其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，应用了最优机器学习模型。通过校准曲线、列线图、决策曲线分析和外部数据集对预测效率进行验证。将支气管肺发育不良样本与对照样本进行比较，分析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，识别出了两个与内质网应激相关的不同分子簇。免疫细胞浸润分析表明，不同簇之间的免疫水平存在显著差异。通过残差、均方根误差以及曲线下面积衡量，支持向量机模型显示出最大的判别能力。最后，开发了一个整合五个基因的支持向量机模型，其在外部验证数据集上表现出优异的性能。通过决策曲线、校准曲线和列线图分析进一步证实了该模型在预测支气管肺发育不良亚型方面的有效性。我们开发了一个潜在的预测模型，以评估内质网应激亚型的风险和支气管肺发育不良患者的临床结局，我们的工作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。
