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

Abstract

Abstract Alzheimer’s disease (AD) is the most common neurodegenerative disorder leading to progressive cognitive decline. With the development of machine learning analysis, screening biomarkers based on existing clinical data is becoming conducive to understanding the pathogenesis of AD and discovering new treatment targets. Our study integrated three AD datasets in the GEO database for differential expression analysis. After constructing a WGCNA network, 109 key genes were obtained and 48 core genes were analyzed from 109 genes using a protein-protein interaction network. The least absolute shrinkage and selection operator, support vector machine recursive feature elimination, and Random Forest methods were applied to obtain the features associated with the 48 core genes and 13 potentially related AD biomarkers were selected. By intersecting InnateDB database with them, we found a potential immune-related marker, UBE2N. MFUZZ cluster analysis revealed that UBE2N is closely related to T cell and B cell functions and the synaptic vesicle cycle signaling pathways. In addition, the expression levels of UBE2N were decreased in the temporal cortex and hippocampus of TauP301S mice but not APP/PS1 mice. Our findings are the first comprehensive identification of UBE2N as a biomarker for AD, paving the way for much-needed early diagnosis and targeted treatment.