Identification and Validation of Metabolism-Related Genes in Alzheimer’s Disease

Abstract Background： Due to its heterogeneity, the pathogenic mechanisms underlying Alzheimer's disease (AD) are not yet fully elucidated. Emerging evidence has demonstrated the critical role of metabolism in the pathophysiology of AD. This study explored the metabolism-related signature genes of AD and precisely identified AD molecular subclasses. Methods: The AD datasets were obtained from the NCBI GEO, and metabolism-relevant genes were downloaded from a previously published compilation. Consensus clustering was utilized to identify AD subclasses. We evaluated the clinic characteristics, correlations with metabolic signatures and immune infiltration of the AD subclasses. Feature genes were screened by WGCNA and processed for GO and KEGG pathway analysis. Furthermore, we used three machine learning algorithms to further narrow down the selection of feature genes. Finally, we identified the diagnostic value and expression of feature genes using dataset and RT-PCR analysis. Results: Three subclasses of AD were identified and designated as MCA, MCB, and MCC. MCA had high AD progression signatures and maybe a high-risk subgroup compared to the other two groups. MCA displayed high glycolysis, fructose and galactose metabolism, whereas citrate cycle and pyruvate metabolism were decreased, associating with high immune infiltration. Conversely, MCB is chiefly involved in the citrate cycle and exhibits elevated expression of immune checkpoint genes. Through WGCNA, a set of 101 metabolic genes were discovered to having the strongest association with the poor progression of AD. Ultimately, the application of machine learning algorithms enabled us to successfully pinpoint eight feature genes. Employing the nomogram based on the eight feature genes translates to distinct clinical benefits for the patients. As indicated by the datasets and RT-PCR analysis, these eight distinctive genes are intimately linked to the advancement of the AD. Conclusion: Metabolic dysfunction is correlated with AD. Hypothetical molecular subclasses based on metabolic genes may provide new insights for individualized therapy of AD. The metabolic feature genes most robust correlation with the advancement of AD were GFAP, CYB5R3, DARS, KIAA0513, EZR, KCNC1, COLEC12 and TST.