机器学习识别阻塞性睡眠呼吸暂停中血管内皮损伤的生物标志物

文献信息

中文摘要

背景： 阻塞性睡眠呼吸暂停（OSA）是一种常见的睡眠障碍，若不治疗可能导致多器官慢性损伤。本研究的目的是使用机器学习方法识别与血管内皮损伤（VED）相关的生物标志物，为OSA的早期诊断和靶向治疗提供见解。
方法： 作者对数据集GSE75097进行差异表达分析以获得差异表达基因（DEGs），并从GeneCards数据库获得1577个VED相关基因（VEDRGs）。两者的交集基因被定义为差异表达的VED相关基因（DE-VEDRGs）。对其进行了功能和通路富集分析。然后，作者采用三种机器学习算法的组合来筛选OSA的特征DE-VEDRGs（FDE-VEDRGs）。此外，作者对FDE-VEDRGs进行了差异、相关性和ROC曲线分析，并对这些基因进行了基因集富集分析（GSEA）和基因集变异分析（GSVA）。最终，在OSA和主要打鼾（PS）样本之间进行了免疫细胞浸润分析，并评估了FDE-VEDRGs表达与免疫细胞浸润水平之间的关系。
结果： 共鉴定出40个DE-VEDRGs。这些基因参与伤口愈合、小GTP酶介导的信号转导、含胶原的细胞外基质、细胞顶端部分、酶抑制剂活性和生长因子受体结合等生物学功能。主要通路包括癌症中的蛋白聚糖、脂质与动脉粥样硬化、癌症中的MicroRNAs、AGE-RAGE和PI3K-Akt信号通路、凋亡等。鉴定出六个OSA的FDE-VEDRGs，即F2RL2、SLC12A1、TGFB2、SPP1、PLAUR和MMP3，并解释了它们的表达差异和相关性。此外，GSEA和GSVA分析揭示了MMP3、PLAUR和SPP1高表达组和低表达组之间在功能和通路富集方面存在显著差异。最后，免疫细胞浸润分析表明，与PS组相比，OSA组中NK细胞活化和巨噬细胞M2的比例显著降低，而中性粒细胞的比例显著增加。相关矩阵显示了每个FDE-VEDRG与每种免疫浸润细胞之间的相关性。六基因逻辑模型在发现数据集中显示出中等判别能力（AUC = 0.78），并在两个外部数据集中表现一致（AUC = 0.75和0.72），提示具有潜在预测价值，值得进一步验证。
结论： 本研究使用整合生物信息学和机器学习分析，在OSA中识别出几个候选血管内皮损伤相关基因。这些发现可能为未来OSA的实验和转化研究提供潜在的生物标志物和初步机制线索。

英文摘要

BACKGROUND: Obstructive Sleep Apnea (OSA) is a common sleep disorder that, if left untreated, may lead to chronic damage to multiple organs. The purpose of this study is to use machine learning methods to identify biomarkers associated with Vascular Endothelial Damage (VED), providing insights for early diagnosis and targeted therapy of OSA.
METHODS: The authors conducted a differential expression analysis on the dataset GSE75097 to obtain Differentially Expressed Genes (DEGs), and obtained 1577 VED-Related Genes (VEDRGs) from the GeneCards database. The intersection genes of the two were defined as the differentially expressed VED-Related Genes (DE-VEDRGs). Functional and pathway enrichment analyses were conducted on them. Then, the authors adopted a combination of three machine learning algorithms to screen the Feature DE-VEDRGs (FDE-VEDRGs) of OSA. Furthermore, the authors conducted differential, correlation, and ROC curve analyses on FDE-VEDRGs, and performed Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) on these genes. Ultimately, an immune cell infiltration analysis was conducted between OSA and Primarily Snore (PS) samples, and the relationship between the expressions of FDE-VEDRGs and the levels of immune cell infiltrations was evaluated.
RESULTS: A total of 40 DE-VEDRGs were identified. These genes were involved in biological functions such as wound healing, small GTPase-mediated signal transduction, collagen-containing extracellular matrix, apical part of the cell, enzyme inhibitor activity, and growth factor receptor binding. The main pathways included Proteoglycans in cancer, Lipid and atherosclerosis, MicroRNAs in cancer, AGE-RAGE and PI3K-Akt signaling pathways, Apoptosis, etc. Six FDE-VEDRGs of OSA, namely F2RL2, SLC12A1, TGFB2, SPP1, PLAUR and MMP3, were identified, and their expression differences and correlations were explained. Furthermore, GSEA and GSVA analyses revealed significant differences in function and pathway enrichment between the high- and low- expression groups of MMP3, PLAUR, and SPP1. Finally, the analysis of immune cell infiltration indicated that compared with the PS group, the proportion of NK cell activation and macrophage M2 in the OSA group was significantly decreased, while the proportion of neutrophils was significantly increased. The correlation matrix shows the correlation between each FDE-VEDRG and each type of immune-infiltrating cells. The six-gene logistic model showed moderate discriminative ability in the discovery dataset (AUC = 0.78) and consistent performance in two external datasets (AUC = 0.75 and 0.72), suggesting potential predictive value that warrants further validation.
CONCLUSIONS: This study identified several candidate vascular endothelial damage-related genes in OSA using integrative bioinformatics and machine learning analyses. These findings may provide potential biomarkers and preliminary mechanistic clues for future experimental and translational studies on OSA.