Identification of diagnostic genes for both Alzheimer’s disease and Metabolic syndrome by the machine learning algorithm - Li, Jinwei; Zhang, Yang; Lu, Tanli; Liang, Rui; Wu, Zhikang; Liu, Meimei; Qin, Linyao; Chen, Hongmou; Yan, Xianlei; Deng, Shan; Zheng, Jiemin; Liu, Quan (Frontiers in Immunology, 2022)
Background: Alzheimer's disease was the most common neurodegenerative disease in the world. Metabolic syndrome was the most common metabolic and endocrine disease in the elderly. Some studies have suggested a possible association between MetS and AD, but few studies of genes that have a co-diagnostic role in both diseases. Methods: The microarray data of AD (GSE63060 and GSE63061 were merged after the batch effect was removed) and MetS (GSE98895) in the GEO database were downloaded. The WGCNA was used to identify the co-expression modules related to AD and MetS. RF and LASSO were used to conducted to identify the candidate genes among AD and MetS. Besides, machine learning XGBoost improves the diagnostic effect of hub gene in AD and MetS. The CIBERSORT algorithm was performed to assess immune cell infiltration into among MetS and AD samples, to investigate the relationship between biomarkers and infiltrating immune cells. the peripheral blood mononuclear cells (PBMCs) single-cell RNA (scRNA) sequencing data from patients with AD and normal were visualized them with Seurat standard flow dimension reduction clustering, assessed the metabolic pathway activity changes of each cell with ssGSEA. Results: the brown module was identified as the significant module with AD and MetS. GO analysis of shared genes showed that intracellular transport enriched, establishment of localization in cell and organelle organization in the pathophysiology of AD and MetS. By using RF and Lasso learning methods, we finally obtained eight diagnostic genes, namely ARHGAP4, SNRPG, UQCRB, PSMA3, DPM1, MED6, RPL36AL and RPS27A. Their AUC were all greater than 0.7. Higher immune cell infiltrations expressions were found in the two diseases and were positively linked to characteristic genes. The scRNA-seq datasets finally obtained seven cell clusters. Seven major cell types including CD8 T cell, monocytes, T cells, NK cell, B cells, dendritic cells and macrophages were clustered according to immune cell markers. The ssGSEA revealed that immune-related gene (SNRPG) was significantly regulated in the glycolysis-metabolic pathway. Conclusion: We identified genes with common diagnostic effects on both MetS and AD, and found genes involved in multiple metabolic pathways associated with a variety of immune cells.