免疫系统
疾病
Lasso(编程语言)
代谢综合征
微阵列
基因
外周血单个核细胞
微阵列分析技术
生物信息学
医学
计算生物学
生物
基因表达
内科学
免疫学
糖尿病
内分泌学
遗传学
计算机科学
万维网
体外
作者
Jinwei Li,Yang Zhang,Tanli Lu,Rui Liang,Zhikang Wu,Meimei Liu,Linyao Qin,Hongmou Chen,Xianlei Yan,Shan Deng,Jiemin Zheng,Quan Liu
标识
DOI:10.3389/fimmu.2022.1037318
摘要
Background Alzheimer’s disease is the most common neurodegenerative disease worldwide. Metabolic syndrome is the most common metabolic and endocrine disease in the elderly. Some studies have suggested a possible association between MetS and AD, but few studied 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 identify the candidate genes. Machine learning XGBoost improves the diagnostic effect of hub gene in AD and MetS. The CIBERSORT algorithm was performed to assess immune cell infiltration MetS and AD samples and 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 individuals were visualized with the Seurat standard flow dimension reduction clustering the metabolic pathway activity changes 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 and establishment of localization in cell and organelle organization were enriched 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 the 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 various immune cells.
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