Identification of UBE2N as a biomarker of Alzheimer’s disease by combining WGCNA with machine learning algorithms

Alzheimer's disease (AD) is the most common cause of dementia, emphasizing the critical need for the development of biomarkers that facilitate accurate and objective assessment of disease progression for early detection and intervention to delay its onset. In our study, three AD datasets from the Gene Expression Omnibus (GEO) database were integrated for differential expression analysis, followed by a weighted gene co-expression network analysis (WGCNA), and potential AD biomarkers were screened. Our study identified UBE2N as a promising biomarker for AD. Functional enrichment analysis revealed that UBE2N is associated with synaptic vesicle cycling and T cell/B cell receptor signaling pathways. Notably, UBE2N expression levels were found to be significantly reduced in the cortex and hippocampus of the Tau^P301S mice. Furthermore, analysis of single-cell data from AD patients demonstrated the association of UBE2N and T cell function. These findings underscore the potential of UBE2N as a valuable biomarker for AD, offering important insights for diagnosis and targeted therapeutic strategies.