Identification of diagnostic genes for both Alzheimer's disease and Metabolic syndrome by the machine learning algorithm.

ABSTRACT:

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 s

SUBMITTER: Li J 

PROVIDER: S-EPMC9667080 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature