Identification of Key Fatty Acid Metabolism-Related Genes in Alzheimer's Disease

Abstract

Alzheimer's disease (AD) is a progressive neurodegenerative disorder, and the role of fatty acid metabolism in its pathogenesis remains incompletely understood. Using AD transcriptome sequencing data from the GEO database, we initially screened for differentially expressed genes and applied Weighted Gene Correlation Network Analysis (WGCNA) to identify crucial gene modules. By intersecting these genes with fatty acid metabolism-related genes (FAMRGs), we obtained AD-related fatty acid metabolism genes (AD-FAMRGs). Subsequently, we conducted KEGG, GO, and Single-sample Gene Set Enrichment Analysis (ssGSEA). Furthermore, we employed three machine learning algorithms to determine the key AD-FAMRGs. Risk genes were thus identified, leading to the construction of a risk model which was subsequently validated through receiver operating characteristic (ROC) curve analysis. Additionally, protein docking studies were performed to assess interactions between key AD-FAMRGs and Tau as well as amyloid beta (Aβ) proteins. To explore potential therapeutic avenues, we searched the DrugBank database for agents targeting these AD-FAMRGs, followed by molecular docking and dynamics simulations. Our investigations highlighted three key AD-FAMRGs: DLD, ELOVL5, and HMGCS1. Functional enrichment analysis indicated their association with metabolism, oxidative stress, and AD pathogenesis. ZDOCK analysis further suggested their interactions with Tau and Aβ proteins, pointing to their possible involvement in AD's pathological processes. ROC analysis demonstrated the predictive accuracy of these AD-FAMRGs, with AUC values ranging from 0.764 to 0.876. Molecular docking and dynamic simulations confirmed the favorable binding of predicted therapeutic agents to these key AD-FAMRGs. Our findings suggest that fatty acid metabolism may be involved in AD pathogenesis, and DLD, ELOVL5, and HMGCS1 may serve as potential therapeutic targets for AD.

Keywords: Alzheimer’s disease; Bioinformatics; Fatty acids metabolism; Machine-learning; Therapeutic target.