Multilayer Proteome and Metabolome-Based Validation Uncovers Combined Regulatory Roles and Predictive Values of 6 RNA...

Existing studies have revealed that RNA modification regulators and cellular senescence can affect the Alzheimer's disease (AD) process. This study investigated the synergistic mechanism in the brains of AD.

Based on brain tissue proteomics of patients with AD, we screened out the subtypes of patients that are coordinately regulated by cellular senescence-related proteins and RNA modification regulators. Transcriptome datasets were used to validate and evaluate 20 hub proteins identified using 100 integrated machine learning algorithms. Finally, protein and metabolic data were employed to explore the characteristics of metabolic subtypes and pathways in AD progression.

The diagnostic model had good diagnostic performance, as revealed by the average area under the receiver operating characteristic curve (AUC) = 0.885 of the internal datasets and the average AUC = 0.89 of transcriptome datasets. Risk score can be used to assess disease progression and the corresponding changes in metabolic characteristics. Finally, metabolic analysis indicates significant abnormalities in amino acid and lipid metabolism during the progression of AD.

We revealed the potential role of RNA modification regulators and cellular senescence-related proteins in AD pathogenesis and related diagnostic markers through proteomic analysis and machine learning-based methods.