Identification of the m6A/m5C/m1A methylation modification genes in Alzheimer's disease based on bioinformatic analysis

Abstract

Background: As a progressive neurodegenerative disease, the comprehensive understanding of the pathogenesis of Alzheimer's disease (AD) is yet to be clarified. Modifications in RNA, including m6A/m5C/m1A, affect the onset and progression of many diseases. Consequently, this study focuses on the role of methylation modification in the pathogenesis of AD.

Materials and methods: Three AD-related datasets, namely GSE33000, GSE122063, and GSE44770, were acquired from GEO. Differential analysis of m6A/m5C/m1A regulator genes was conducted. Applying a consensus clustering approach, distinct subtypes within AD were identified as per the expression patterns of relevant differentially expressed genes. Machine learning models were constructed to identify five significant genes from the best model. The analysis of hub gene-based drug regulatory networks and ceRNA regulatory networks was conducted by Cytoscape.

Results: In comparison to non-AD patients, 24 genes were identified as dysregulated in AD patients, and these genes were associated with various immunological characteristics. Two distinct clusters were successfully identified through consensus clustering, with cluster 2 demonstrating higher immune characteristics compared to cluster 1. The performance of four machine learning models was determined by conducting a receiver operating characteristic (ROC) analysis. The analysis revealed that the SVM model achieved the highest AUC value of 0.947. Five genes (YTHDF1, METTL3, DNMT1, DNMT3A, ALKBH1) were selected as the predicted genes. Finally, a hub gene-based Gene-Drug regulatory network and a ceRNA regulatory network were successfully developed.

Conclusions: The findings offered fresh perspectives on the molecular patterns and immune mechanisms underlying AD, contributing valuable insights into our understanding of this complex neurodegenerative disorder.

Keywords: Alzheimer’s disease; cluster analysis; diagnostic model; immune characteristics; machine learning.

Figure 1

Study flow chart.

Figure 2

Differential analysis of three regulators of methylation modification in Alzheimer’s disease. (A) Heatmap illustrating the expression data of 24 regulators. (B) Chromosomal localization of 24 regulators. (C, D) Analysis of the correlation between the 24 differentially expressed regulatory factors, with red indicating positive association and green indicating negative association. The correlation coefficient is represented by the pie chart area. (E) Boxplots demonstrating variations in immune infiltration between AD and non-AD controls. *P < 0.05, **P < 0.01, ***P < 0.001. (F) Correlation analysis of 24 methylated differential genes with 22 immune cell types. *P < 0.05, **P < 0.01, ***P < 0.001.

Figure 3

Determination of molecular clusters related to m1A, m5C, and m6A in AD. (A) Consensus clustering matrix when k = 2. (B) PCA analysis. (C) Boxplots illustrating the expression of 24 DEGs between two clusters. (D) Heatmap displaying a differential expression of 24 DEGs between the two clusters. (E) Relative percentages of 22 infiltrated immune cells between two clusters. *P<0.05, **P<0.01, ***P<0.001. (F) Boxplots depicting variations in immune infiltration between two clusters. * P < 0.05, ** P < 0.01, ***P < 0.001.

Figure 4

GSVA biological functional analysis. (A) The KEGG pathway analysis. (B) The GO function analysis.

Figure 5

Development of a suitable diagnostic model through four machine learning and validation models. (A) Distribution of cumulative residuals for each machine learning model. (B) Boxplots illustrating the residuals of all machine learning models. (C) The salient characteristics of the RF, SVM, GLM, and XGB machine learning models. (D) Four machine learning models tested utilising a fivefold cross-validation procedure, with results examined utilising the ROC curve. (E) ROC analysis of the 5-gene-based SVM model in GSE33000 dataset. (F) ROC analysis of the 5-gene-based SVM model in GSE122063 dataset. (G) ROC analysis of the 5-gene-based SVM model in GSE44770 dataset.

Figure 6

Correlation analysis between hub genes and immune characteristics. (A) Correlation between hub genes and immune cells shown by CIBERSORT analysis. (B) Association between hub genes and immune cells depicted by ssGSEA analysis. The colour spectrum, ranging from red to purple, illustrates the transition from positive to negative associations, respectively. A high number of asterisks and darker-coloured modules depict stronger associations. *P < 0.05; **P < 0.01; ****P < 0.001.

Figure 7

Single-gene GSEA-KEGG pathway analysis. (A) ALKBH1. (B) DNMT1. (C) DNMT3A. (D) METTL3. (E) YTHDF1.

Figure 8

High and low expression groups per the expression levels of each marker gene combined with GSVA. (A) ALKBH1. (B) DNMT1. (C) DNMT3A. (D) METTL3. (E) YTHDF1.

Figure 9

Validation of the five-gene based on the SVM model. (A) Development of a nomogram utilising the 5-gene based on the SVM model to predict the risk of AD patients. (B) Assessment of the prognostic efficacy of the nomogram model through a calibration curve. (C) Utilisation of discriminant analysis for evaluating the sensitivity of the nomogram to change.

Figure 10

Prediction of marker gene-targeted drugs.

Figure 11

Construction of a network of ceRNA based on hub genes.