RESUMO
BACKGROUND: Alzheimer's disease (AD) is a widespread neurodegenerative disease that primarily affects the elderly. Unfortunately, the lack of convenient early diagnostic tools makes it difficult to intervene and treat the disease during its initial stages. METHODS: We obtained four bulk and single-cell RNA-sequencing peripheral blood samples related to AD from public databases. Using Boruta and LASSO machine learning algorithms, we screened the signature genes and constructed a diagnostic model using lightGBM. The model was further validated in a test cohort. Additionally, we extracted hub biomarkers using the protein-protein interactions method and validated them in a single-cell RNA-seq dataset. RESULTS: Our analysis revealed the identification of 37 AD-related peripheral blood signature genes, with their main enrichment in ribosome-related biological functions. Four core biomarkers, RPL24, RPL5, RPS27A, and RPS4X, were identified and exhibited good diagnostic power in the testing cohort. Immune infiltration analysis revealed a higher proportion of CD4+ T cells in AD patients' peripheral blood compared to healthy controls, with a negative correlation with the four ribosome-associated core genes. Validation in a single-cell RNA-seq dataset confirmed these findings. CONCLUSIONS: Ribosomal family proteins have the potential to serve as biomarkers for the diagnosis and treatment of AD, and are associated with CD4+ T cell activation.
