Application of weighted co-expression network analysis and machine learning to identify the pathological mechanism of Alzheimer's disease.

ABSTRACT: Aberrant deposits of neurofibrillary tangles (NFT), the main characteristic of Alzheimer's disease (AD), are highly related to cognitive impairment. However, the pathological mechanism of NFT formation is still unclear. This study explored differences in gene expression patterns in multiple brain regions [entorhinal, temporal, and frontal cortex (EC, TC, FC)] with distinct Braak stages (0- VI), and identified the hub genes via weighted gene co-expression network analysis (WGCNA) and machine learning. For WGCNA, consensus modules were detected and correlated with the single sample gene set enrichment analysis (ssGSEA) scores. Overlapping the differentially expressed genes (DEGs, Braak stages 0 vs. I-VI) with that in the interest module, metascape analysis, and Random Forest were cond

SUBMITTER: Chai K 

PROVIDER: S-EPMC9326231 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature