Fig. 1

Correlation of 4 RNA modifications with AD. A For the experimental flowchart of this study, we analyzed the effects of 4 types of RNA modifications and 18 types of PCDs on AD, respectively. Subsequently, a joint analysis was performed to screen PCD genes related to RNA modifications to explore their potential roles in AD. Key genes were screened by unsupervised consensus clustering and WGCNA and risk scores were calculated. Patients were categorized into high-risk and low-risk groups based on risk scores to assess the impact of risk scores on disease progression. Finally, we assessed the diagnostic effect of risk scores on AD using ROC curves, which were validated using multiple external datasets (By Figdraw). B Box plots showed significant changes in the expression levels of four RNA modification regulators between the AD and Control groups. T-test was used to detect differential expression of regulators between AD and control groups. p-value: * < 0.05; ** < 0.01; *** < 0.001; **** < 0.0001. C Correlation analysis between the expression of RNA regulatory factors and CDR, Braak and PlaqueMean Volcano Plots. Horizontal coordinates indicate the magnitude of the correlation and vertical coordinates are − log10 (p-value). Red dots indicate positively correlated genes and blue dots indicate negatively correlated genes. D, E LASSO regression visualization of the MSBB RNA modifier cohort. The optimal λ is obtained when the partial likelihood of the deviation is minimized. F RNAM score and ROC analysis curves for the RNA modifier used to calculate the RNAM score