Li Xin-Yu, Li Kun, Wei Yi-Han, Yu Wen-Kai, Wu Jing-Hao, Gao Kai, He Wen-Jun, Niu Peng-Peng, Zhang Chan, Cheng Yu-Nan, Li Yu-Sheng
Department of Neurology, the First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Dong Road, Zhengzhou, Henan, China.
Henan Engineering Research Center of Neural Function Detection and Regulation, Zhengzhou, Henan, China.
Sci Rep. 2025 Jul 20;15(1):26365. doi: 10.1038/s41598-025-12553-y.
Cerebral amyloid angiopathy (CAA) and insomnia are age-related neurological disorders increasingly recognized as being closely associated. However, research on the shared genes and their biological mechanisms remains limited. This study aims to identify common genes between CAA and insomnia and explore their potential molecular mechanisms, offering new insights for diagnosis and treatment. Blood samples were collected from 11 CAA patients and 11 healthy controls, followed by RNA sequencing (RNA-seq). Additionally, the microarray dataset GSE208668 for the insomnia cohort was downloaded from the Gene Expression Omnibus (GEO) database. Differential expression analysis was performed to identify common differentially expressed genes (DEGs). Protein-protein interaction (PPI) networks and machine learning methods Random Forest (RF) and Extreme Gradient Boosting (XGBoost) were used to narrow down key genes. We explored the biological functions of these genes through immune cell infiltration, metabolic and Hallmark pathway analyses, and clinical correlation analysis. Co-expression networks, drug-mRNA networks, transcription factor (TF)-mRNA-miRNA networks, and competing endogenous RNA (ceRNA) networks were also constructed. Finally, hub gene expression patterns were analyzed using the Human Protein Atlas (HPA) database, and validation was performed in clinical samples and animal models using quantitative Reverse Transcription Polymerase Chain Reaction (qRT-PCR) and Western blot. Differential expression analysis identified 185 DEGs. PPI network construction and machine learning methods identified CBX5 and POLR1B as common hub genes for both insomnia and CAA. Immune infiltration, metabolic, and Hallmark pathway analyses revealed these hub genes play distinct roles in each disease. Various network models were constructed to explore their regulatory mechanisms. The reliability of the hub genes was validated using bioinformatics analyses and experimental approaches. This study, combining bioinformatics and experimental validation, identifies CBX5 and POLR1B as shared hub genes for CAA and insomnia. These findings offer new molecular targets for the diagnosis and treatment of both diseases, providing a foundation for future research.
脑淀粉样血管病(CAA)和失眠是与年龄相关的神经系统疾病,越来越被认为密切相关。然而,关于共享基因及其生物学机制的研究仍然有限。本研究旨在识别CAA和失眠之间的共同基因,并探索其潜在的分子机制,为诊断和治疗提供新的见解。从11例CAA患者和11名健康对照中采集血样,随后进行RNA测序(RNA-seq)。此外,从基因表达综合数据库(GEO)下载了失眠队列的微阵列数据集GSE208668。进行差异表达分析以识别共同的差异表达基因(DEG)。使用蛋白质-蛋白质相互作用(PPI)网络以及机器学习方法随机森林(RF)和极端梯度提升(XGBoost)来缩小关键基因范围。我们通过免疫细胞浸润、代谢和特征通路分析以及临床相关性分析来探索这些基因的生物学功能。还构建了共表达网络、药物-mRNA网络、转录因子(TF)-mRNA-miRNA网络和竞争性内源RNA(ceRNA)网络。最后,使用人类蛋白质图谱(HPA)数据库分析枢纽基因表达模式,并使用定量逆转录聚合酶链反应(qRT-PCR)和蛋白质免疫印迹法在临床样本和动物模型中进行验证。差异表达分析鉴定出185个DEG。PPI网络构建和机器学习方法确定CBX5和POLR1B为失眠和CAA的共同枢纽基因。免疫浸润、代谢和特征通路分析表明这些枢纽基因在每种疾病中发挥不同作用。构建了各种网络模型以探索其调控机制。使用生物信息学分析和实验方法验证了枢纽基因的可靠性。本研究结合生物信息学和实验验证,确定CBX5和POLR1B为CAA和失眠的共享枢纽基因。这些发现为这两种疾病的诊断和治疗提供了新的分子靶点,为未来研究奠定了基础。