通过机器学习和多层转录组整合及功能验证，鉴定JAK2和ANXA5为连接阻塞性睡眠呼吸暂停与氧化应激的关键基因

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中文摘要

阻塞性睡眠呼吸暂停（OSA）是一种常见且严重的睡眠障碍，与氧化应激（OS）密切相关。本研究旨在通过生物信息学方法识别并验证与OSA相关的潜在OS相关基因。我们通过结合limma检验、加权相关网络分析（WGCNA）以及GeneCards数据库中的OS相关基因，成功识别了OS相关差异表达基因（OS-DEGs）。进一步利用基因本体（GO）和京都基因与基因组百科全书（KEGG）富集分析、蛋白质-蛋白质相互作用（PPI）网络分析、Lasso回归分析、随机森林算法和支持向量机递归特征消除（SVM-RFE）方法，识别关键基因及其潜在生物学作用。通过受试者工作特征（ROC）曲线分析评估和验证关键基因的准确性。使用人类单细胞RNA测序（scRNA-seq）数据集进行细胞分类注释、关键基因单细胞表达谱分析，以及基于scTenifoldKnk算法的虚拟基因敲除实验。整合scRNA-seq测序、拟时序轨迹推断、细胞-细胞通讯分析和bulk免疫浸润解卷积，揭示了OSA中的单核细胞亚型重塑。最后，使用实时定量PCR（RT-qPCR）和Western blotting验证临床样本中关键基因的表达水平。共识别出57个共同DEGs，表明在OS、炎症和肿瘤通路中显著富集，尤其在免疫代谢通路中突出。通过整合DEGs、WGCNA、PPI结果和机器学习方法，筛选出关键基因Janus激酶2（JAK2）和ANXA5。JAK2在疾病条件下显著上调，而ANXA5显著下调。ROC曲线表现出高准确性（曲线下面积[AUC] > 0.85）。人类scRNA-seq分析显示，关键基因主要在单核细胞中高表达。虚拟敲除实验表明，这些关键基因在调节免疫反应和炎症反应中起关键作用。PPI网络和富集分析验证了下游基因S100P、ALOX5AP、PROK2和PADI4可能协同参与免疫反应和炎症调节。最后，临床样本实验进一步验证了生物信息学分析的结果。本研究通过整合多层转录组学和机器学习技术，为未来OSA的诊断、机制研究和治疗开发提供了新的研究见解。

英文摘要

Obstructive sleep apnea (OSA) is a common and severe sleep disorder closely associated with oxidative stress (OS). This study aims to identify and validate potential OS-related genes associated with OSA through bioinformatics methods. We successfully identified OS-related differentially expressed genes (OS-DEGs) by combining the limma test, weighted correlation network analysis (WGCNA), and OS-related genes from the GeneCards database. Key genes and potential biological roles were further identified using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), enrichment analysis, protein-protein interaction (PPI) network analysis, Lasso regression analysis, random forest algorithm, and support vector machine recursive feature elimination (SVM-RFE) method. Evaluate and validate the accuracy of key genes through receiver operating characteristic (ROC) curve analysis. The human single-cell RNA sequencing (scRNA-seq) dataset is used for cell classification annotation, analysis of key gene single-cell expression profiles, and virtual gene knockout experiments based on the scTenifoldKnk algorithm. Integrating scRNA-seq sequencing, pseudotime trajectory inference, cell-cell communication analysis, and bulk immune infiltration deconvolution reveals monocyte subtype remodeling in OSA. Finally, the expression levels of key genes in clinical samples were validated using real-time quantitative PCR (RT-qPCR) and Western blotting. A total of 57 common DEGs, indicating significant enrichment in OS, inflammation, and tumor pathways, particularly prominent in the immunometabolism pathway. By integrating DEGs, WGCNA, PPI results, and machine learning methods, key genes Janus kinase 2 (JAK2) and ANXA5 were screened out. JAK2 was significantly upregulated under disease conditions, while ANXA5 was significantly downregulated. ROC curve exhibited high accuracy (area under the curve [AUC] > 0.85). Human scRNA-seq analysis revealed that key genes were predominantly highly expressed in monocytes. Virtual knockout experiments demonstrated that these key genes play a crucial role in regulating immune responses and inflammatory reactions. PPI networks and enrichment analysis verified that downstream genes S100P, ALOX5AP, PROK2, and PADI4 may collaboratively participate in immune response and inflammation regulation. Finally, clinical sample experiment further validated the results of bioinformatics analysis. This study provides new research insights for the diagnosis, mechanism research, and treatment development of OSA in the future by integrating multilayer transcriptomic and machine learning techniques.