Abstract
BACKGROUND: RNA modification plays a critical role in Alzheimer's disease (AD) by modulating the expression and function of AD-related genes, thereby affecting AD occurrence and progression. Programmed cell death is closely related to neuronal death and associated with neuronal loss and cognitive function changes in AD. However, the mechanism of their joint action on AD remains unknown and requires further exploration. METHODS: We used the MSBB RNA-seq dataset to analyze the correlation between RNA modification, programmed cell death, and AD. We used combined studies of RNA modification and programmed cell death to distinguish subgroups of patients, and the results highlight the strong correlation between RNA modification-related programmed cell death and AD. A weighted gene co-expression network was constructed, and the pivotal roles of programmed cell death genes in key modules were identified. Finally, by combining unsupervised consensus clustering, gene co-expression networks, and machine learning algorithms, an RNA modification-related programmed cell death network was constructed, and the pivotal roles of programmed cell death genes in key modules were identified. An RNA modification-related programmed cell death risk score was calculated to predict the occurrence of AD. RESULTS: RPCD-related genes classified patients into subgroups with distinct clinical characteristics. Nineteen key genes were identified and an RPCD risk score was constructed based on the key genes. This score can be used for the diagnosis of AD and the assessment of disease progression in patients. The diagnostic efficacy of the RPCD risk score and the key genes was validated in the ROSMAP, GEO, and ADNI datasets. CONCLUSION: This study uncovered that RNA modification-related PCD is of significance for AD progression and early prediction, providing insights from a new perspective for the study of disease mechanisms in AD.