Molecular Subgroup Classification in Alzheimer's Disease by Transcriptomic Profiles.

Abstract

Alzheimer's disease (AD) is a progressive cognitive disorder that occurs worldwide, and the lack of disease-modifying targets and pathways is a pressing issue. This study aimed to provide new targets and pathways by performing molecular subgroup classification. After normalizing the collected data, the subgroup number was confirmed with consensus clustering. Comparisons of clinical features among subgroups were conducted to clarify the clinical traits of each subgroup. Subgroup-specific genes were identified to perform weighted gene coexpression analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were carried out. Next, gene set enrichment analysis (GSEA) was performed. Protein-protein interaction networks were built to screen core genes and in each subgroup to perform Spearman correlation analysis with clinical traits. Sequencing profiles of 1068 AD samples collected from 2 datasets were classified into 3 subgroups. Clinical comparisons revealed that patients in subgroup III tended to be younger, while their pathological grades were the most severe. WGCNA detected four gene modules, and the turquoise module, where the dopaminergic synapse pathway was enriched, was related to subgroup I. The neurotrophin signaling pathway and TGF-beta signaling pathway were robustly enriched in the blue and brown modules, respectively, in subgroup III. Moreover, 3 hub genes in subgroup I were negatively correlated with the sum of neurofibrillary tangle (Nft) density. Conversely, hub genes in subgroups II and III exhibited positive correlations with the sum of Nft density. These results provide new pathways and targets for AD treatment.