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.
Keywords: AD; Ubiquitin-Protease; bioinformatic; biomarkers; machine learning.
© 2023 The Author(s). Published by IMR Press.