Identification of genes related to glucose metabolism and analysis of the immune characteristics in Alzheimer's disease

Abstract

Objective: Glucose metabolism plays a crucial role in the progression of Alzheimer's disease (AD). The purpose of this study is to identify genes related to glucose metabolism in AD by bioinformatics, construct an early AD prediction model from the perspective of glucose metabolism, and analyze the characteristics of immune cell infiltration.

Methods: AD-related modules and genes were screened by weighted gene co-expression network analysis (WGCNA). The GO and KEEG enrichment analysis were used to explore the potential biological functions of glucose metabolism related genes (GMRGs) in AD. The Least Absolute Shrinkage Selection Operator (LASSO) method was used to construct an early AD prediction model based on GMRGs. Then, the receiver operating characteristic curve (ROC) and nomogram were introduced to evaluate the effectiveness of this model. Finally, CIBERSORT and single-cell analysis were applied for illustrating the immune characteristics in AD patients.

Results: A total of 462 differential expressed genes (DEGs) were obtained between Non-Alzheimer's disease (ND,) and AD groups. The genes in the blue module had the highest correlation with AD by WGCNA analysis. We found 18 intersected genes among DEGs, blue model genes and GMRGs according to the Venn diagram. The GO and KEEG enrichment analysis showed that these 18 genes were mainly involved in the production of metabolites and energy, glycolysis, amino acid biosynthesis and so on. The early AD prediction model including ENO2, TPI1, AEBP1, HERC1, PCSK1, PREPL, SLC25A4, UQCRC2, CHST6, DDIT4, ACSS1 and SUCLA2 was constructed by LASSO analysis. The area under the curve (AUC) of this model in brain tissues was 0.942. Then, we draw the nomogram of this model and the C-index was 0.942. The model was further validated in blood samples and the AUC was 0.644. Immune cell infiltration analysis showed that the proportion of plasma cells, T cells follicular helper and activated NK cells in AD group were significantly lower than ND group, while the proportion of M1 macrophages, neutrophils, T cells CD4 naive and γ-δ T cells was significantly increased when compared with the ND group. Additionally, the specific GMRGs such as ENO2, DDIT4, and SUCLA2 are significantly correlated with certain immune cells such as plasma cells, follicular helper T cells, and M1 macrophages. Single-cell analysis results suggested that the increased macrophages in AD was associated with the up-regulation of AEBP1, DDIT4 and ACSS1.

Conclusions: The diagnosis model based on the twelve GMRGs has strong predictive ability and can be used as early diagnosis biomarkers for AD. In addition, these GMRGs closely associate with AD development by influencing the glucose metabolism of immune cells.

Keywords: Alzheimer's disease; Bioinformatic analysis; Gene; Glucose metabolism; Immune infiltration.