Gao Simin, Shan Dan, Tang Yuedi
Department of Otolaryngology-Head and Neck Surgery, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Department of Otolaryngology-Head and Neck Surgery, Sleep Medicine Center, West China School of Public Health and West China Forth Hospital, Sichuan University, Chengdu, Sichuan, China.
Front Neurol. 2024 Oct 1;15:1452507. doi: 10.3389/fneur.2024.1452507. eCollection 2024.
Obstructive sleep apnea (OSA) syndrome is a prevalent form of respiratory sleep disorder, with an increasing prevalence among children. The consequences of OSA include obesity, diabetes, cardiovascular disease, and neuropsychological diseases. Despite its pervasive impact, a significant proportion of individuals especially children remain unaware that they suffer from OSA. Consequently, there is an urgent need for an accessible diagnostic approach. In this study, we conducted a bioinformatic analysis to identify potential biomarkers from a proteomics dataset comprising serum samples from children with OSA in the progression stage. In the Gene Set Enrichment Analysis (GSEA), we observed that the complement and immune response pathways persisted throughout the development of OSA and could be detected in the early stages. Subsequent to soft clustering and WGCNA analysis, it was revealed that the Hippo pathway, including ITGAL and FERMT3, plays a role in mild OSA. The analysis revealed a significant alteration of the complement and coagulation pathways, including TFPI and MLB2, in moderate OSA. In severe OSA, there was an association between hypoxia and the extracellular matrix (ECM) receptor interaction and collagen binding. In summary, it can be posited that the systemic inflammation may persist throughout the progression of OSA. Furthermore, severe OSA is characterized by abnormal vascular endothelial function, which may be attributed to chronic hypoxia. Finally, four potential biomarkers (ITGAL, TFPI, TTR, ANTXR1) were identified based on LASSO regression, and a prediction model for OSA progression was constructed based on the biomarkers.
阻塞性睡眠呼吸暂停（OSA）综合征是一种常见的呼吸睡眠障碍形式，在儿童中的患病率呈上升趋势。OSA的后果包括肥胖、糖尿病、心血管疾病和神经心理疾病。尽管其影响广泛，但很大一部分人，尤其是儿童，仍然不知道自己患有OSA。因此，迫切需要一种易于使用的诊断方法。在本研究中，我们进行了生物信息学分析，以从一个蛋白质组学数据集中识别潜在的生物标志物，该数据集包含处于进展期的OSA儿童的血清样本。在基因集富集分析（GSEA）中，我们观察到补体和免疫反应途径在OSA的整个发展过程中持续存在，并且可以在早期阶段检测到。在软聚类和加权基因共表达网络分析（WGCNA）之后，发现包括整合素αL（ITGAL）和桩蛋白3（FERMT3）在内的Hippo信号通路在轻度OSA中起作用。分析显示，在中度OSA中，包括组织因子途径抑制物（TFPI）和MLB2在内的补体和凝血途径发生了显著改变。在重度OSA中，缺氧与细胞外基质（ECM）受体相互作用和胶原结合之间存在关联。总之，可以推测全身炎症可能在OSA的整个进展过程中持续存在。此外，重度OSA的特征是血管内皮功能异常，这可能归因于慢性缺氧。最后，基于套索回归（LASSO）识别出四个潜在的生物标志物（ITGAL、TFPI、甲状腺素转运蛋白（TTR）、炭疽毒素受体1（ANTXR1）），并基于这些生物标志物构建了OSA进展的预测模型。
