PDF(Pubmed)
Abstract：
UNASSIGNED: Sleep disorders (SD) are known to have a profound impact on human health and quality of life although their exact pathogenic mechanisms remain poorly understood.
UNASSIGNED: The study first accessed SD datasets from the GEO and identified DEGs. These DEGs were then subjected to gene set enrichment analysis. Several advanced techniques, including the RF, SVM-RFE, PPI networks, and LASSO methodologies, were utilized to identify hub genes closely associated with SD. Additionally, the ssGSEA approach was employed to analyze immune cell infiltration and functional gene set scores in SD. DEGs were also scrutinized in relation to miRNA, and the DGIdb database was used to explore potential pharmacological treatments for SD. Furthermore, in an SD murine model, the expression levels of these hub genes were confirmed through RT-qPCR and Western Blot analyses.
UNASSIGNED: The findings of the study indicate that DEGs are significantly enriched in functions and pathways related to immune cell activity, stress response, and neural system regulation. The analysis of immunoinfiltration demonstrated a marked elevation in the levels of Activated CD4+ T cells and CD8+ T cells in the SD cohort, accompanied by a notable rise in Central memory CD4 T cells, Central memory CD8 T cells, and Natural killer T cells. Using machine learning algorithms, the study also identified hub genes closely associated with SD, including IPO9, RAP2A, DDX17, MBNL2, PIK3AP1, and ZNF385A. Based on these genes, an SD diagnostic model was constructed and its efficacy validated across multiple datasets. In the SD murine model, the mRNA and protein expressions of these 6 hub genes were found to be consistent with the results of the bioinformatics analysis.
UNASSIGNED: In conclusion, this study identified 6 genes closely linked to SD, which may play pivotal roles in neural system development, the immune microenvironment, and inflammatory responses. Additionally, the key gene-based SD diagnostic model constructed in this study, validated on multiple datasets showed a high degree of reliability and accuracy, predicting its wide potential for clinical applications. However, limited by the range of data sources and sample size, this may affect the generalizability of the results.
UNASSIGNED: The study first accessed SD datasets from the GEO and identified DEGs. These DEGs were then subjected to gene set enrichment analysis. Several advanced techniques, including the RF, SVM-RFE, PPI networks, and LASSO methodologies, were utilized to identify hub genes closely associated with SD. Additionally, the ssGSEA approach was employed to analyze immune cell infiltration and functional gene set scores in SD. DEGs were also scrutinized in relation to miRNA, and the DGIdb database was used to explore potential pharmacological treatments for SD. Furthermore, in an SD murine model, the expression levels of these hub genes were confirmed through RT-qPCR and Western Blot analyses.
UNASSIGNED: The findings of the study indicate that DEGs are significantly enriched in functions and pathways related to immune cell activity, stress response, and neural system regulation. The analysis of immunoinfiltration demonstrated a marked elevation in the levels of Activated CD4+ T cells and CD8+ T cells in the SD cohort, accompanied by a notable rise in Central memory CD4 T cells, Central memory CD8 T cells, and Natural killer T cells. Using machine learning algorithms, the study also identified hub genes closely associated with SD, including IPO9, RAP2A, DDX17, MBNL2, PIK3AP1, and ZNF385A. Based on these genes, an SD diagnostic model was constructed and its efficacy validated across multiple datasets. In the SD murine model, the mRNA and protein expressions of these 6 hub genes were found to be consistent with the results of the bioinformatics analysis.
UNASSIGNED: In conclusion, this study identified 6 genes closely linked to SD, which may play pivotal roles in neural system development, the immune microenvironment, and inflammatory responses. Additionally, the key gene-based SD diagnostic model constructed in this study, validated on multiple datasets showed a high degree of reliability and accuracy, predicting its wide potential for clinical applications. However, limited by the range of data sources and sample size, this may affect the generalizability of the results.
摘要：
■已知睡眠障碍（SD）对人类健康和生活质量具有深远的影响，尽管对其确切的致病机制知之甚少。
■该研究首先从GEO访问SD数据集并确定DEG。然后对这些DEGs进行基因集富集分析。几种先进的技术，包括RF,SVM-RFE,PPI网络，和LASSO方法，用于鉴定与SD密切相关的hub基因。此外,采用ssGSEA方法分析SD中的免疫细胞浸润和功能基因集评分。DEGs与miRNA的关系也进行了审查，DGIdb数据库用于探索SD的潜在药物治疗。此外，在SD鼠模型中，通过RT-qPCR和WesternBlot分析证实了这些hub基因的表达水平.
■研究结果表明，DEGs在与免疫细胞活性相关的功能和途径方面显着富集，应激反应，和神经系统调节。免疫浸润分析显示SD队列中激活的CD4+T细胞和CD8+T细胞水平显著升高，伴随着中央记忆CD4T细胞的显着增加，中央记忆CD8T细胞，自然杀伤T细胞。使用机器学习算法，该研究还确定了与SD密切相关的hub基因，包括IPO9，RAP2A，DDX17、MBNL2、PIK3AP1和ZNF385A。基于这些基因,构建SD诊断模型,并在多个数据集上验证其有效性.在SD鼠模型中,发现这6个hub基因的mRNA和蛋白表达与生物信息学分析结果一致。
■总而言之，这项研究确定了6个与SD密切相关的基因，它们可能在神经系统发育中起关键作用，免疫微环境，和炎症反应。此外,本研究构建的基于关键基因的SD诊断模型,在多个数据集上验证显示出高度的可靠性和准确性，预测其临床应用的广泛潜力。然而,受数据源范围和样本量的限制，这可能会影响结果的概括性。
■该研究首先从GEO访问SD数据集并确定DEG。然后对这些DEGs进行基因集富集分析。几种先进的技术，包括RF,SVM-RFE,PPI网络，和LASSO方法，用于鉴定与SD密切相关的hub基因。此外,采用ssGSEA方法分析SD中的免疫细胞浸润和功能基因集评分。DEGs与miRNA的关系也进行了审查，DGIdb数据库用于探索SD的潜在药物治疗。此外，在SD鼠模型中，通过RT-qPCR和WesternBlot分析证实了这些hub基因的表达水平.
■研究结果表明，DEGs在与免疫细胞活性相关的功能和途径方面显着富集，应激反应，和神经系统调节。免疫浸润分析显示SD队列中激活的CD4+T细胞和CD8+T细胞水平显著升高，伴随着中央记忆CD4T细胞的显着增加，中央记忆CD8T细胞，自然杀伤T细胞。使用机器学习算法，该研究还确定了与SD密切相关的hub基因，包括IPO9，RAP2A，DDX17、MBNL2、PIK3AP1和ZNF385A。基于这些基因,构建SD诊断模型,并在多个数据集上验证其有效性.在SD鼠模型中,发现这6个hub基因的mRNA和蛋白表达与生物信息学分析结果一致。
■总而言之，这项研究确定了6个与SD密切相关的基因，它们可能在神经系统发育中起关键作用，免疫微环境，和炎症反应。此外,本研究构建的基于关键基因的SD诊断模型,在多个数据集上验证显示出高度的可靠性和准确性，预测其临床应用的广泛潜力。然而,受数据源范围和样本量的限制，这可能会影响结果的概括性。
