Identification of feature genes and pathways for Alzheimer's disease via WGCNA and LASSO regression.

ABSTRACT: While Alzheimer's disease (AD) can cause a severe economic burden, the specific pathogenesis involved is yet to be elucidated. To identify feature genes associated with AD, we downloaded data from three GEO databases: GSE122063, GSE15222, and GSE138260. In the filtering, we used AD for search keywords, Homo sapiens for species selection, and established a sample size of > 20 for each data set, and each data set contains Including the normal group and AD group. The datasets GSE15222 and GSE138260 were combined as a training group to build a model, and GSE122063 was used as a test group to verify the model's accuracy. The genes with differential expression found in the combined datasets were used for analysis through Gene Ontology (GO) and The Kyoto Encyclopedia of Genes and Genome Pathways

SUBMITTER: Sun H 

PROVIDER: S-EPMC9536257 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature