ArticleFrontiers in aging neuroscience2026
FLOT1 and EEF1D: ac4C-related genes bridging Alzheimer's disease and sleep deprivation.
Article in Frontiers in aging neuroscience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Alzheimer's disease (AD) and sleep deprivation (SD), two common conditions in the elderly, share complex molecular connections and may mutually influence each other's pathogenesis. Current drugs only relieve symptoms with limited efficacy, making it urgent to explore the shared pathological mechanisms and potential intervention targets of the two conditions. This study used bioinformatics: first screening AD-related genes associated with SD and N4-acetylcytidine (ac4C) from relevant data; then identifying key genes via Mendelian randomization (MR) analysis and machine learning; finally screening AD-related key cells with single-cell RNA sequencing (scRNA-seq) data, to provide a basis for revealing the molecular and cellular regulatory mechanisms of AD-SD comorbidity. Methods: This study integrated bulk RNA sequencing (RNA-Seq) and scRNA-seq data from the Gene Expression Omnibus (GEO) database to identify AD-related key genes associated with SD and ac4C. Machine learning algorithms, including MR, were applied to screen these key genes. Additionally, gene set enrichment analysis (GSEA) was conducted to explore the pathways associated with the key genes, while ssGSEA was used to assess differences in immune cell infiltration. For the scRNA-seq data, key cells involved in AD pathology were further identified. Subsequently, the differential expression of the two key genes was validated using peripheral blood samples collected from AD and SD patients. Results: Through MR analysis, machine learning algorithms, and other analytical approaches, FLOT1 and EEF1D were identified as key genes. GSEA revealed that these key genes were enriched in multiple pathways, including the lysosome pathway, chemokine signaling pathway, and leukocyte transendothelial migration. Immune cell infiltration analysis suggested that myeloid-derived suppressor cells (MDSCs) might serve as key immune cells. Additionally, scRNA-seq analysis identified microglia, CD4 + T cells, CD8 + T cells, and natural killer (NK) cells as key cell types involved in AD pathogenesis. Critically, these key genes were successfully validated in peripheral blood samples from AD and SD patients, aligning with the above analysis. Conclusion: Overall, FLOT1 and EEF1D were identified as key genes associated with SD and ac4C in AD. This finding provided new grounds for the clinical diagnosis and treatment of AD.
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