Development of a novel immune infiltration-related diagnostic model for Alzheimer's disease using bioinformatic strategies.

ABSTRACT:

Background

The pathogenesis of Alzheimer's disease (AD) is complex and multi-factorial. Increasing evidence has shown the important role of immune infiltration in AD. Thus the current study was designed to identify immune infiltration-related genes and to explore their diagnostic value in AD.

Methods

The expression data of AD patients were downloaded from the GEO database. The limma R package identified differentially expressed genes (DEGs) between AD and controls. The CIBERSORT algorithm identified differentially infiltrated immune cells (DIICs) between AD and controls. DIIC-correlated DEGs were obtained by Pearson correlation analysis. WGCNA was employed to identify DIIC-related modules. Next, LASSO, RFE, and RF machine learning methods were applied to screen robust DIIC-r

SUBMITTER: Zhuang X 

PROVIDER: S-EPMC10400274 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature