Using Bioinformatics and Machine Learning to Predict the Genetic Characteristics of Ferroptosis-Cuproptosis-Related Genes Associated with Sleep Deprivation.

IF 3 2区医学 Q2 CLINICAL NEUROLOGY

Nature and Science of Sleep Pub Date : 2024-09-24 eCollection Date: 2024-01-01 DOI:10.2147/NSS.S473022

Liang Wang, Shuo Wang, Chujiao Tian, Tao Zou, Yunshan Zhao, Shaodan Li, Minghui Yang, Ningli Chai

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引用次数: 0

Abstract

Purpose: Sleep deprivation (SD), a common sleep disease in clinic, has certain risks, and its pathogenesis is still unclear. This study aimed to identify ferroptosis-cuproptosis-related genes (FCRGs) associated with SD through bioinformatics and machine learning, thus elucidating their biological significance and clinical value.

Methods: SD-DEGs were obtained from GEO. We intersected key WGCNA module genes of DE-FCRGs with SD-DEGs to obtain SD-DE-FCRGs. GO and KEGG analyses were performed. Machine learning was used to screen SD-DE-FCRGs, and filtered genes were intersected to obtain SD characteristic genes. ROC curves were used to evaluate the accuracy of SD characteristic genes. CIBERSORT was used to analyze the correlation between SD-DE-FCRGs and immune cells. We constructed a ceRNA network of SD-DE-FCRGs and used DGIbd to predict gene drug targets.

Results: The 156 DEGs were identified from GSE98566. Five SD-DE-FCRGs from DE- FCRGs and SD-DEGs were analyzed via WGCNA, and enrichment analysis involved mainly ribosome regulation, mitochondrial pathways, and neurodegenerative diseases. Machine learning was used to obtain Four SD-DE-FCRGs (IKZF1, JCHAIN, MGST3, and UQCR11), and these gene analyses accurately evaluated the distribution model (AUC=0.793). Immune infiltration revealed that SD hub genes were correlated with most immune cells. Unsupervised cluster analysis revealed significant differential expression of immune-related genes between two subtypes. GSVA and GSEA revealed that enriched biological functions included oxidative phosphorylation, ribonucleic acid, metabolic diseases, activation of oxidative phosphorylation, and other pathways. Four SD-DE-FCRGs associated with 29 miRNAs were identified via the construction of a ceRNA network. The important target lenalidomide of IKZF1 was predicted.

Conclusion: We first used bioinformatics and machine learning to screen four SD-DE-FCRGs. These genes may affect the involvement of infiltrating immune cells in pathogenesis of SD by regulating FCRGs. We predicted that lenalidomide may target IKZF1 from SD-DE-FCRGs.

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利用生物信息学和机器学习预测与睡眠不足有关的铁突变-杯突相关基因的遗传特征。

目的：睡眠不足（SD）是临床上常见的睡眠疾病，具有一定的危害性，其发病机制尚不清楚。本研究旨在通过生物信息学和机器学习识别与SD相关的铁-杯突相关基因（FCRGs），从而阐明其生物学意义和临床价值：SD-DEGs来自GEO。我们将 DE-FCRGs 的关键 WGCNA 模块基因与 SD-DEGs 相交，得到 SD-DE-FCRGs。进行了GO和KEGG分析。使用机器学习筛选SD-DE-FCRGs，并将筛选出的基因进行交叉以获得SD特征基因。使用 ROC 曲线评估 SD 特征基因的准确性。CIBERSORT用于分析SD-DE-FCRGs与免疫细胞之间的相关性。我们构建了SD-DE-FCRGs的ceRNA网络，并使用DGIbd预测基因药物靶点：结果：从 GSE98566 中鉴定出 156 个 DEGs。通过 WGCNA 分析了 DE- FCRGs 和 SD-DEGs 中的 5 个 SD-DE-FCRGs，富集分析主要涉及核糖体调控、线粒体通路和神经退行性疾病。利用机器学习获得了4个SD-DE-FCRGs（IKZF1、JCHAIN、MGST3和UQCR11），这些基因分析准确评估了分布模型（AUC=0.793）。免疫浸润显示，SD 中心基因与大多数免疫细胞相关。无监督聚类分析显示，两个亚型之间的免疫相关基因表达存在显著差异。GSVA和GSEA显示，富集的生物功能包括氧化磷酸化、核糖核酸、代谢疾病、激活氧化磷酸化和其他途径。通过构建ceRNA网络，发现了与29个miRNA相关的4个SD-DE-FCRGs。预测了IKZF1的重要靶点来那度胺：我们首先利用生物信息学和机器学习筛选出了四个SD-DE-FCRGs。结论：我们首先利用生物信息学和机器学习技术筛选出了4个SD-DE-FCRGs，这些基因可能通过调控FCRGs影响浸润免疫细胞参与SD的发病机制。我们预测来那度胺可能会从SD-DE-FCRGs中靶向IKZF1。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature and Science of Sleep Neuroscience-Behavioral Neuroscience

CiteScore

5.70

自引率

5.90%

发文量

245

审稿时长

16 weeks

期刊介绍： Nature and Science of Sleep is an international, peer-reviewed, open access journal covering all aspects of sleep science and sleep medicine, including the neurophysiology and functions of sleep, the genetics of sleep, sleep and society, biological rhythms, dreaming, sleep disorders and therapy, and strategies to optimize healthy sleep. Specific topics covered in the journal include: The functions of sleep in humans and other animals Physiological and neurophysiological changes with sleep The genetics of sleep and sleep differences The neurotransmitters, receptors and pathways involved in controlling both sleep and wakefulness Behavioral and pharmacological interventions aimed at improving sleep, and improving wakefulness Sleep changes with development and with age Sleep and reproduction (e.g., changes across the menstrual cycle, with pregnancy and menopause) The science and nature of dreams Sleep disorders Impact of sleep and sleep disorders on health, daytime function and quality of life Sleep problems secondary to clinical disorders Interaction of society with sleep (e.g., consequences of shift work, occupational health, public health) The microbiome and sleep Chronotherapy Impact of circadian rhythms on sleep, physiology, cognition and health Mechanisms controlling circadian rhythms, centrally and peripherally Impact of circadian rhythm disruptions (including night shift work, jet lag and social jet lag) on sleep, physiology, cognition and health Behavioral and pharmacological interventions aimed at reducing adverse effects of circadian-related sleep disruption Assessment of technologies and biomarkers for measuring sleep and/or circadian rhythms Epigenetic markers of sleep or circadian disruption.