Identification and immune characteristics of molecular subtypes related to protein glycosylation in Alzheimer's disease

Abstract

Background: Protein glycosylation has been confirmed to be involved in the pathological mechanisms of Alzheimer's disease (AD); however, there is still a lack of systematic analysis of the immune processes mediated by protein glycosylation-related genes (PGRGs) in AD.

Materials and methods: Transcriptomic data of AD patients were obtained from the Gene Expression Omnibus database and divided into training and verification datasets. The core PGRGs of the training set were identified by weighted gene co-expression network analysis, and protein glycosylation-related subtypes in AD were identified based on k-means unsupervised clustering. Protein glycosylation scores and neuroinflammatory levels of different subtypes were compared, and functional enrichment analysis and drug prediction were performed based on the differentially expressed genes (DEGs) between the subtypes. A random forest model was used to select important DEGs as diagnostic markers between subtypes, and a line chart model was constructed and verified in other datasets. We evaluated the differences in immune cell infiltration between the subtypes through the single-sample gene set enrichment analysis, analyzed the correlation between core diagnostic markers and immune cells, and explored the expression regulation network of the core diagnostic markers.

Results: Eight core PGRGs were differentially expressed between the training set and control samples. AD was divided into two subtypes with significantly different biological processes, such as vesicle-mediated transport in synapses and neuroactive ligand-receptor interactions. The high protein glycosylation subtype had a higher level of neuroinflammation. Riluzole and sulfasalazine were found to have potential clinical value in this subtype. A reliable construction line chart model was constructed based on nine diagnostic markers, and SERPINA3 was identified as the core diagnostic marker. There were significant differences in immune cell infiltration between the two subtypes. SERPINA3 was found to be closely related to immune cells, and the expression of SERPINA3 in AD was found to be regulated by a competing endogenous RNA network that involves eight long non-coding RNAs and seven microRNAs.

Conclusion: Protein glycosylation and its corresponding immune process play an important role in the occurrence and development of AD. Understanding the role of PGRGs in AD may provide a new potential therapeutic target for AD.

Keywords: Alzheimer’s disease; diagnostic model; immune cells; molecular subtypes; protein glycosylation-related genes.

FIGURE 1

Identification of core protein glycosylation-related genes (PGRGs) in Alzheimer’s disease (AD). (A) Weighted gene co-expression network analysis (WGCNA) analysis was performed on the training set, resulting in a cluster dendrogram of co-expressed genes. (B) The soft threshold of a scale-free network. (C) The module-character relationship was constructed, with each module containing the corresponding correlation and P-value. (D) Transcription factors that regulate the expression of genes are represented by the MElightgreen module and their interactions. (E) Differentially expressed PGRGs between the training set and control samples. (F) The intersection of genes represented by the differentially expressed PGRGs and MElightgreen modules between the training set and control samples. (G) The co-expression relationship of 7 core PGRGs.

FIGURE 2

The two distinct protein glycosylation-related subtypes in Alzheimer’s disease (AD) identified by unsupervised clustering of eight protein glycosylation-related genes (PGRGs). (A) Consensus clustering cumulative distribution function (CDF) for k = 2–9. (B) Heatmap of the matrix of co-occurrence proportions of AD samples. (C) Principal component analysis (PCA) is used to determine the discrimination of A and B subtypes. (D) The difference in the expression of eight core PGRGs between the two subtypes. (E) Differentially expressed genes between the subtypes. (F) GO analysis of differentially expressed genes between the subtypes reveals related biological processes, molecular functions, and cellular components. (G) Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differentially expressed genes between the subtypes.

FIGURE 3

Differences in protein glycosylation levels, neuroinflammation, and drug prediction between the subtypes. (A,B) GSEA analysis of subtypes A and B. (C) The difference in glycosylation level between the subtypes. (D) The distribution proportion of protein glycosylation level in each subtype. (E) Differences in expression levels of cytokines and inflammatory signal transduction molecules between the subtypes. *P < 0.05, **P < 0.01, and ***P < 0.001. (F) The correlation between potential therapeutic drugs and corresponding targets. (G) The binding conformation of SLC7A11 and riluzole (binding energy = –7.8 kcal/mol). (H) The binding conformation of SLC7A11 and sulfasalazine (binding energy = –94.0 kcal/mol).

FIGURE 4

Construction and verification of the diagnostic line diagram model (A) Random forest trees constructed by cross-validation. (B) Genes with an importance score higher than 2. (C) A line chart was used to predict different protein glycosylation levels in patients with Alzheimer’s disease (AD). (D) A calibration curve that evaluates the predictive ability of the line chart model. (E) The decision curve analysis (DCA) curve was used to evaluate the clinical value of the line chart model. (F) The clinical impact curve of the line chart model constructed based on the DCA curve. (G) The difference in the SERPINA3 gene expression between AD and control samples. (H) The difference in the GFAP gene expression between AD and control samples. (I) Receiver operating characteristic curve of the SERPINA3 gene in the validation set. (J) Receiver operating characteristic curve of the GFAP gene in the validation set.

FIGURE 5

Immune cell infiltration profiles across the key diagnostic marker subtypes and expression regulatory networks. (A) The correlation between the expression of eight core PGRGs and immune cell infiltration. (B) The correlation between the SERPINA3 gene and immune cells. (C) The intersection of miRNAs by which the expression of SERPINA3 is regulated in four databases. (D) The regulatory ceRNA network of SERPINA3 expression.