Bioinformatic Analysis and Experimental Validation of Ubiquitin-Proteasomal System-Related Hub Genes as Novel Biomarkers for Alzheimer's Disease

免疫系统 疾病 基因 计算生物学 生物 生物标志物 基因表达 生物信息学 免疫学 医学 遗传学 病理
作者
Yuting Zhang,Jie Wu,Guoxing You,Xu‐Guang Guo,Yupeng Wang,Yu Zhiyong,Yan Geng,Qinghua Zhong,Jie Zan,Linbo Zheng
出处
期刊:Journal of Integrative Neuroscience [Imperial College Press]
卷期号:22 (6): 138-138 被引量:2
标识
DOI:10.31083/j.jin2206138
摘要

Background: Alzheimer’s disease (AD) is a common progressive neurodegenerative disease. The Ubiquitin-Protease system (UPS), which plays important roles in maintaining protein homeostasis in eukaryotic cells, is involved in the development of AD. This study sought to identify differential UPS-related genes (UPGs) in AD patients by using bioinformatic methods, reveal potential biomarkers for early detection of AD, and investigate the association between the identified biomarkers and immune cell infiltration in AD. Methods: The differentially expressed UPGs were screened with bioinformatics analyses using the Gene Expression Omnibus (GEO) database. A weighted gene co-expression network analysis (WGCNA) analysis was performed to explore the key gene modules associated with AD. A Single-sample Gene Set Enrichment Analysis (ssGSEA) analysis was peformed to explore the patterns of immune cells in the brain tissue of AD patients. Real-time quantitative PCR (RT-qPCR) was performed to examine the expression of hub genes in blood samples from healthy controls and AD patients. Results: In this study, we identified four UPGs (USP3, HECW2, PSMB7, and UBE2V1) using multiple bioinformatic analyses. Furthermore, three UPGs (USP3, HECW2, PSMB7) that are strongly correlated with the clinical features of AD were used to construct risk score prediction markers to diagnose and predict the severity of AD. Subsequently, we analyzed the patterns of immune cells in the brain tissue of AD patients and the associations between immune cells and the three key UPGs. Finally, the risk score model was verified in several datasets of AD and showed good accuracy. Conclusions: Three key UPGs are identified as potential biomarker for AD patients. These genes may provide new targets for the early identification of AD patients.
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