Zeng Shun, Liu Nannan, Zhang Andong, Duan Na, Xu Bo, Ai Chunqi
Department of Mental Health Center, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei Province, China.
Department of Sleep Disorders Center, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei Province, China.
BMC Psychiatry. 2025 Apr 14;25(1):371. doi: 10.1186/s12888-024-06395-7.
Chronic sleep deprivation can lead to cognitive impairment which makes it difficult to think, focus, and make comprehensive decisions. This in turn leads to the progression and increased risk of several diseases. This study aimed to explore potential drug targets and biomarkers underlying the increased disease risk due to sleep deprivation, including stress responses, immune dysfunction, and metabolic dysregulation. Four datasets namely GSE40562, GSE98566, GSE98582 for sleep deprivation, and GSE26576 normal brain cells were utilized to understand the molecular basis and potential drug targets associated with sleep deprivation. The GEO2R tool, Robust rank aggregations, and Venny were used to retrieve the common DEGs. Functional gene and pathway analyses were carried out via GO and the KEGG analyses. The STRING and CytoHuba plugins were utilized to identify the protein-protein interactions (PPIs) as well as the hub genes in the main PPI subnetworks following the drug interaction of the hub genes and GEPIA-based survival analysis of the DEGs. A total of 160 common DEGs were retrieved from all four datasets. Among them, 65 were down-regulated and 95 were up-regulated. TOP2A, AURKB, NEFL, CDC42, ASPM, GAP43, PVALB, NUF2, CALM1, TPR, KIF5B, KIF15, TROAP, NDC80, PBK, MKI67, SST, AHSP, ALAS2, and NEFH were retrieved as hub genes. While based on drug interaction, survival analysis and gene expression profile eight hub gene named TOP2A, AURKB, PVALB, CALM1, KIF5B, PBK, MKI67, and SST were found to be potential drug candidates and significantly correlated with infiltration levels of CD8 + T cells, B cells, macrophages, CD4 + T cells, neutrophils, and dendritic cells. These genes might play a role in sleep disorders via various pathways associated with neurodegeneration and diseases, potentially serving as biomarkers to support treatment and diagnosis.
长期睡眠剥夺会导致认知障碍，使人难以思考、集中注意力和做出全面决策。这反过来又会导致多种疾病的进展和风险增加。本研究旨在探索睡眠剥夺导致疾病风险增加背后的潜在药物靶点和生物标志物，包括应激反应、免疫功能障碍和代谢失调。利用四个数据集，即睡眠剥夺相关的GSE40562、GSE98566、GSE98582以及正常脑细胞的GSE26576，来了解与睡眠剥夺相关的分子基础和潜在药物靶点。使用GEO2R工具、稳健秩聚合和Venny来检索共同的差异表达基因（DEGs）。通过基因本体论（GO）和京都基因与基因组百科全书（KEGG）分析进行功能基因和通路分析。利用STRING和CytoHuba插件来识别蛋白质-蛋白质相互作用（PPI）以及枢纽基因在主要PPI子网中的情况，随后对枢纽基因进行药物相互作用和基于基因表达谱交互分析（GEPIA）的差异表达基因生存分析。从所有四个数据集中总共检索到160个共同的差异表达基因。其中，65个下调，95个上调。检索到TOP2A、AURKB、NEFL、CDC42、ASPM、GAP43、PVALB、NUF2、CALM1、TPR、KIF5B、KIF15、TROAP、NDC80、PBK、MKI67、SST、AHSP、ALAS2和NEFH作为枢纽基因。而基于药物相互作用、生存分析和基因表达谱，发现八个枢纽基因TOP2A、AURKB、PVALB、CALM1、KIF5B、PBK、MKI67和SST是潜在的药物候选物，并且与CD8 + T细胞、B细胞、巨噬细胞、CD4 + T细胞、中性粒细胞和树突状细胞的浸润水平显著相关。这些基因可能通过与神经退行性变和疾病相关的各种途径在睡眠障碍中发挥作用，有可能作为支持治疗和诊断的生物标志物。
