阐明睡眠障碍：功能基因集和枢纽基因的综合生物信息学分析。

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

背景：尽管睡眠障碍（SD）对人类健康和生活质量有深远影响，但确切的发病机制仍知之甚少。

方法：本研究首先从 GEO 中获取 SD 数据集并鉴定差异表达基因（DEGs）。然后对这些 DEGs 进行基因集富集分析。利用随机森林（RF）、支持向量机递归特征消除（SVM-RFE）、蛋白质-蛋白质相互作用（PPI）网络和 LASSO 等多种先进技术，识别与 SD 密切相关的关键基因。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。还研究了 DEGs 与 miRNA 的关系，并利用 DGIdb 数据库探索 SD 的潜在药物治疗方法。此外，在 SD 小鼠模型中，通过 RT-qPCR 和 Western blot 分析验证这些关键基因的表达水平。

结果：研究结果表明，DEGs 在与免疫细胞活性、应激反应和神经系统调节相关的功能和途径中显著富集。免疫浸润分析表明，SD 组中活化的 CD4+T 细胞和 CD8+T 细胞水平显著升高，同时中央记忆 CD4 T 细胞、中央记忆 CD8 T 细胞和自然杀伤 T 细胞也显著升高。使用机器学习算法，还鉴定出与 SD 密切相关的关键基因，包括 IPO9、RAP2A、DDX17、MBNL2、PIK3AP1 和 ZNF385A。基于这些基因，构建了 SD 诊断模型，并在多个数据集上验证了其疗效。在 SD 小鼠模型中，这些 6 个关键基因的 mRNA 和蛋白表达与生物信息学分析结果一致。

结论：总之，本研究鉴定了与 SD 密切相关的 6 个基因，这些基因可能在神经系统发育、免疫微环境和炎症反应中发挥重要作用。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具有高度可靠性和准确性，预示其在临床应用方面具有广泛的潜力。然而，受限于数据源的范围和样本量，这可能会影响结果的普遍性。