Identification of metabolism-related subtypes and feature genes in Alzheimer's disease

Abstract

Background: Owing to the heterogeneity of Alzheimer's disease (AD), its pathogenic mechanisms are yet to be fully elucidated. Evidence suggests an important role of metabolism in the pathophysiology of AD. Herein, we identified the metabolism-related AD subtypes and feature genes.

Methods: The AD datasets were obtained from the Gene Expression Omnibus database and the metabolism-relevant genes were downloaded from a previously published compilation. Consensus clustering was performed to identify the AD subclasses. The clinical characteristics, correlations with metabolic signatures, and immune infiltration of the AD subclasses were evaluated. Feature genes were screened using weighted correlation network analysis (WGCNA) and processed via Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Furthermore, three machine-learning algorithms were used to narrow down the selection of the feature genes. Finally, we identified the diagnostic value and expression of the feature genes using the AD dataset and quantitative reverse-transcription polymerase chain reaction (qRT-PCR) analysis.

Results: Three AD subclasses were identified, namely Metabolism Correlated (MC) A (MCA), MCB, and MCC subclasses. MCA contained signatures associated with high AD progression and may represent a high-risk subclass compared with the other two subclasses. MCA exhibited a high expression of genes related to glycolysis, fructose, and galactose metabolism, whereas genes associated with the citrate cycle and pyruvate metabolism were downregulated and associated with high immune infiltration. Conversely, MCB was associated with citrate cycle genes and exhibited elevated expression of immune checkpoint genes. Using WGCNA, 101 metabolic genes were identified to exhibit the strongest association with poor AD progression. Finally, the application of machine-learning algorithms enabled us to successfully identify eight feature genes, which were employed to develop a nomogram model that could bring distinct clinical benefits for patients with AD. As indicated by the AD datasets and qRT-PCR analysis, these genes were intimately associated with AD progression.

Conclusion: Metabolic dysfunction is associated with AD. Hypothetical molecular subclasses of AD based on metabolic genes may provide new insights for developing individualized therapy for AD. The feature genes highly correlated with AD progression included GFAP, CYB5R3, DARS, KIAA0513, EZR, KCNC1, COLEC12, and TST.

Keywords: Alzheimer’s disease; Characteristic genes; Immune infiltration; Machine learning; Metabolic subclass.