Multi-tissue Methylation Analysis of Alzheimer's Disease: Insights into Pathways, Modules, and Key Genes

DNA methylation plays a crucial role in the onset and progression of Alzheimer's disease (AD). Genome-wide methylation analysis of multi-tissue data can provide insights into the pathology and diagnostic biomarkers of AD. Computational tools were employed to identify pathways associated with AD and to develop a poly-methylation score (PMS). Key genes within the identified pathways were determined through module analysis and protein-protein interaction networks followed by validation in β-amyloid 42-induced cellular models. Linear mixed-effects model was used to investigate the longitudinal relationship between PMS and changes in AD phenotypes. AD-related pathways exhibited tissue specificity. The key genes in blood, frontal cortex, neurons, and glial cells were THBS1, TGFB1, HIF1A, and KLF4, respectively. Furthermore, the expression alterations of these genes were validated in three cellular models (SH-SY5Y, HMC3, and THP-1). Notably, higher PMS was significantly correlated with accelerated declines in cerebral metabolic rate and cognitive function. Using machine learning to analyze methylation data and identify key genes in AD patients enhanced our understanding of AD pathogenesis. Further research is needed to validate the potential of these key genes as intervention targets for AD.