Stroke, a result of acute cerebrovascular disease that causes cerebral dysfunction, often coexists with depression or even major depressive disorder (MDD). Despite the recognized significance of lipid metabolism disorders in both stroke and depression, their interwoven role in the pathogenesis of these conditions remains largely uncharted. This study sourced transcriptomic data linked to stroke and depression from the GEO database. Hub genes were identified through weighted gene coexpression network analysis (WGCNA) and machine learning algorithms. The diagnostic efficacy of the model featuring hub genes was evaluated using receiver operating characteristic (ROC) curve analyses and nomogram plots. Enrichment analysis and immune infiltration were examined while potential therapeutic agents were predicted using the drug profile database. The expression levels of the hub genes were verified on peripheral blood samples using quantitative Real-Time Polymerase Chain Reaction (qRT-PCR). 6 differentially expressed genes (DEGs) related to lipid metabolism were identified showing significant enrichment in metabolic and immune pathways. The diagnostic model constructed based on these genes demonstrated robust performance across multiple datasets. Gene set enrichment analysis (GSEA) suggested the involvement of nucleic acid metabolism and olfactory transduction in both diseases. Immune infiltration analysis revealed significant differences among various immune cells, such as monocytes and neutrophils. 11 potential drugs targeting at least two hub genes were identified. The exploration of lipid metabolism-related diagnostic genes offers valuable insights into the potential interplay between stroke and depression.
Liu et al. (2026) studied this question.