PDF(Pubmed)
Abstract：
UNASSIGNED: Obstructive sleep apnea (OSA) syndrome and nonalcoholic fatty liver disease (NAFLD) have been shown to have a close association in previous studies, but their pathogeneses are unclear. This study explores the molecular mechanisms associated with the pathogenesis of OSA and NAFLD and identifies key predictive genes.
UNASSIGNED: Using the Gene Expression Omnibus (GEO) database, we obtained gene expression profiles GSE38792 for OSA and GSE89632 for NAFLD and related clinical characteristics. Mitochondrial unfolded protein response-related genes (UPRmtRGs) were acquired by collating and collecting UPRmtRGs from the GeneCards database and relevant literature from PubMed. The differentially expressed genes (DEGs) associated with OSA and NAFLD were identified using differential expression analysis. Gene Set Enrichment Analysis (GSEA) was conducted for signaling pathway enrichment analysis of related disease genes. Based on the STRING database, protein-protein interaction (PPI) analysis was performed on differentially co-expressed genes (Co-DEGs), and the Cytoscape software (version 3.9.1) was used to visualize the PPI network model. In addition, the GeneMANIA website was used to predict and construct the functional similar genes of the selected Co-DEGs. Key predictor genes were analyzed using the receiver operating characteristic (ROC) curve.
UNASSIGNED: The intersection of differentially expressed genes shared between OSA and NAFLD-related gene expression profiles with UPRmtRGs yielded four Co-DEGs: ASS1, HDAC2, SIRT3, and VEGFA. GSEA obtained the relevant enrichment signaling pathways for OSA and NAFLD. PPI network results showed that all four Co-DEGs interacted (except for ASS1 and HDAC2). Ultimately, key predictor genes were selected in the ROC curve, including HDAC2 (OSA: AUC = 0.812; NAFLD: AUC = 0.729), SIRT3 (OSA: AUC = 0.775; NAFLD: AUC = 0.750), and VEGFA (OSA: AUC = 0.812; NAFLD: AUC = 0.861) (they have a high degree of accuracy in predicting whether a subject will develop two diseases).
UNASSIGNED: In this study, four co-expression differential genes for OSA and NAFLD were obtained, and they can predict the occurrence of both diseases. Transcriptional mechanisms involved in OSA and NAFLD interactions may be better understood by exploring these key genes. Simultaneously, this study provides potential diagnostic and therapeutic markers for patients with OSA and NAFLD.
UNASSIGNED: Using the Gene Expression Omnibus (GEO) database, we obtained gene expression profiles GSE38792 for OSA and GSE89632 for NAFLD and related clinical characteristics. Mitochondrial unfolded protein response-related genes (UPRmtRGs) were acquired by collating and collecting UPRmtRGs from the GeneCards database and relevant literature from PubMed. The differentially expressed genes (DEGs) associated with OSA and NAFLD were identified using differential expression analysis. Gene Set Enrichment Analysis (GSEA) was conducted for signaling pathway enrichment analysis of related disease genes. Based on the STRING database, protein-protein interaction (PPI) analysis was performed on differentially co-expressed genes (Co-DEGs), and the Cytoscape software (version 3.9.1) was used to visualize the PPI network model. In addition, the GeneMANIA website was used to predict and construct the functional similar genes of the selected Co-DEGs. Key predictor genes were analyzed using the receiver operating characteristic (ROC) curve.
UNASSIGNED: The intersection of differentially expressed genes shared between OSA and NAFLD-related gene expression profiles with UPRmtRGs yielded four Co-DEGs: ASS1, HDAC2, SIRT3, and VEGFA. GSEA obtained the relevant enrichment signaling pathways for OSA and NAFLD. PPI network results showed that all four Co-DEGs interacted (except for ASS1 and HDAC2). Ultimately, key predictor genes were selected in the ROC curve, including HDAC2 (OSA: AUC = 0.812; NAFLD: AUC = 0.729), SIRT3 (OSA: AUC = 0.775; NAFLD: AUC = 0.750), and VEGFA (OSA: AUC = 0.812; NAFLD: AUC = 0.861) (they have a high degree of accuracy in predicting whether a subject will develop two diseases).
UNASSIGNED: In this study, four co-expression differential genes for OSA and NAFLD were obtained, and they can predict the occurrence of both diseases. Transcriptional mechanisms involved in OSA and NAFLD interactions may be better understood by exploring these key genes. Simultaneously, this study provides potential diagnostic and therapeutic markers for patients with OSA and NAFLD.
摘要：
■阻塞性睡眠呼吸暂停（OSA）综合征和非酒精性脂肪性肝病（NAFLD）在以前的研究中已被证明有密切的关联，但其发病机制尚不清楚.本研究探讨了与OSA和NAFLD发病机制相关的分子机制，并确定了关键预测基因。
■使用基因表达综合（GEO）数据库，我们获得了OSA的基因表达谱GSE38792和NAFLD的基因表达谱GSE89632和相关临床特征。通过从GeneCards数据库和PubMed的相关文献中整理和收集UPRmtRG，获得了线粒体未折叠的蛋白质反应相关基因（UPRmtRG）。使用差异表达分析鉴定与OSA和NAFLD相关的差异表达基因(DEGs)。基因集富集分析(GSEA)用于相关疾病基因的信号通路富集分析。基于STRING数据库，对差异共表达基因(Co-DEGs)进行蛋白质-蛋白质相互作用(PPI)分析，并使用Cytoscape软件（3.9.1版）可视化PPI网络模型。此外,使用GeneMANIA网站预测和构建所选Co-DEGs的功能相似基因。使用接受者工作特征（ROC）曲线分析关键预测基因。
■OSA和NAFLD相关基因表达谱与UPRmtRGs之间共有的差异表达基因的交集产生了四个Co-DEGs：ASS1、HDAC2、SIRT3和VEGFA。GSEA获得了OSA和NAFLD的相关富集信号通路。PPI网络结果表明，所有四个Co-DEGs相互作用（ASS1和HDAC2除外）。最终,在ROC曲线中选择关键预测基因，包括HDAC2（OSA：AUC=0.812；NAFLD：AUC=0.729），SIRT3（OSA：AUC=0.775；NAFLD：AUC=0.750），和VEGFA(OSA:AUC=0.812;NAFLD:AUC=0.861)(它们在预测受试者是否会发展两种疾病方面具有高度的准确性)。
■在这项研究中，获得了OSA和NAFLD的四个共表达差异基因，他们可以预测这两种疾病的发生。通过探索这些关键基因，可以更好地理解OSA和NAFLD相互作用中涉及的转录机制。同时,这项研究为OSA和NAFLD患者提供了潜在的诊断和治疗标志物.
■使用基因表达综合（GEO）数据库，我们获得了OSA的基因表达谱GSE38792和NAFLD的基因表达谱GSE89632和相关临床特征。通过从GeneCards数据库和PubMed的相关文献中整理和收集UPRmtRG，获得了线粒体未折叠的蛋白质反应相关基因（UPRmtRG）。使用差异表达分析鉴定与OSA和NAFLD相关的差异表达基因(DEGs)。基因集富集分析(GSEA)用于相关疾病基因的信号通路富集分析。基于STRING数据库，对差异共表达基因(Co-DEGs)进行蛋白质-蛋白质相互作用(PPI)分析，并使用Cytoscape软件（3.9.1版）可视化PPI网络模型。此外,使用GeneMANIA网站预测和构建所选Co-DEGs的功能相似基因。使用接受者工作特征（ROC）曲线分析关键预测基因。
■OSA和NAFLD相关基因表达谱与UPRmtRGs之间共有的差异表达基因的交集产生了四个Co-DEGs：ASS1、HDAC2、SIRT3和VEGFA。GSEA获得了OSA和NAFLD的相关富集信号通路。PPI网络结果表明，所有四个Co-DEGs相互作用（ASS1和HDAC2除外）。最终,在ROC曲线中选择关键预测基因，包括HDAC2（OSA：AUC=0.812；NAFLD：AUC=0.729），SIRT3（OSA：AUC=0.775；NAFLD：AUC=0.750），和VEGFA(OSA:AUC=0.812;NAFLD:AUC=0.861)(它们在预测受试者是否会发展两种疾病方面具有高度的准确性)。
■在这项研究中，获得了OSA和NAFLD的四个共表达差异基因，他们可以预测这两种疾病的发生。通过探索这些关键基因，可以更好地理解OSA和NAFLD相互作用中涉及的转录机制。同时,这项研究为OSA和NAFLD患者提供了潜在的诊断和治疗标志物.
