Integrated machine learning–based RNA sequencing and single-cell analysis reveal RNA methylation regulation patterns in the immune microenvironment of Alzheimer’s disease

Abstract Alterations in RNA methylation may affect the initiation and development of Alzheimer’s disease. However, the exact nature of the relationship between RNA methylation and Alzheimer’s disease remains unclear. In this study, RNA methylation levels were analyzed by bulk transcriptomic and single-cell RNA sequencing. The expression levels of RNA methylation regulators were confirmed using molecular biology techniques. Co-expression network analysis was used to identify relevant long non-coding RNAs. Molecular subtypes related to RNA methylation were classified, and variations in clinical characteristics, biological behavior, and immune signatures between subtypes were assessed. Machine learning approaches were applied to identify methylation-associated long noncoding RNAs, which were used to construct a risk model and nomogram for Alzheimer’s disease. Potential therapeutic agents for different risk groups were predicted, and in vitro experiments were conducted to identify key RNA methylation events. Single-cell analysis demonstrated enhanced RNA methylation in patients with Alzheimer’s disease, particularly within T cells, B cells, and NK cells. Quantitative reverse transcription-polymerase chain reaction and western blot confirmed alterations in RNA methylation regulators in neurons treated with amyloid-β oligomers in vitro . This evidence supported the classification of patients with Alzheimer’s disease into heterogeneous subtypes. Specifically, subtype 1 was identified as the immune-active subtype, while subtype 2 was characterized by a metabolic phenotype. Machine learning algorithms identified five significant methylation-associated long non-coding RNAs –LINC01007, MAP4K3-DT, MIR302CHG, VAC14-AS1, and TGFB2-OT1–that accurately predict clinical outcomes for patients with Alzheimer’s disease. These patients were classified into low- and high-risk categories; the latter group displayed higher immune infiltration, upregulated immune regulatory gene expression, and elevated immune scores and responded better to treatment with arachidonic-trifluoroethane. These findings suggest that dysregulated RNA methylation alters the immune microenvironment in Alzheimer’s disease and is closely associated with its progression. This phenomenon provides novel insights into potential therapeutic strategies for Alzheimer’s disease that target RNA methylation.