CNS neuroscience & therapeutics · 2026
Transcriptomic Insights Into Alzheimer's Disease: Differentially Expressed Genes and Cholesterol Metabolism.
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Sun Rui, Wang Xu, Wang Zaibao, Li Chunliu, Shao Qing, Liu Xiangru, Zhu Hongrui, Wang Sheng, He Keqiang
Аннотация
BACKGROUND: Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline and memory impairment, posing significant challenges to affected individuals, their families, and healthcare systems globally. With projections indicating that the prevalence of AD could escalate to 152 million cases by 2050, there is an urgent need to elucidate the underlying mechanisms driving this condition. Additionally, developing effective diagnostic tools to aid in its early detection and management is crucial.
METHODS: In this study, we utilized a combination of Mendelian randomization and advanced machine learning techniques to analyze transcriptomic data from five distinct cohorts of Alzheimer's Disease (AD) patients. After addressing batch effects, we identified differentially expressed genes (DEGs) between the AD and control groups. Mendelian randomization analysis was conducted to assess the causal relationships between DEGs and AD risk. A Venn diagram was subsequently used to identify genes associated with cholesterol metabolism from the screened gene set. The shared DEGs were subjected to functional enrichment analyses. Furthermore, immune analysis was quantified using Gene Set Enrichment Analysis (GSEA). A diagnostic model for AD was developed by evaluating 113 combinations of 12 machine learning algorithms with 10-fold cross-validation on the training datasets, followed by external validation on test datasets. Finally, immunofluorescence staining was performed on mouse brain slices to verify the expression level of KLHL21.
RESULTS: Our analyses identified a substantial number of differentially expressed genes (DEGs) demonstrating significant differences between Alzheimer's disease (AD) patients and control groups. Among these, we identified 29 genes associated with AD, with 21 of them linked to cholesterol metabolism, highlighting its pivotal role in the disease's pathogenesis. From this set, we developed a robust 8-gene diagnostic signature (comprising CHSY1, FIBP, DHCR24, HVCN1, KIFAP3, KLHL21, LETMD1, and SLC25A29), which outperformed existing AD diagnostic models in both training and testing cohorts. Additionally, complementary animal experiments were conducted to validate the biological relevance of these genes, further elucidating their roles in AD pathology.
CONCLUSIONS: Our research identified critical genes and proposed novel pathways for early diagnosis and potential therapeutic interventions, paving the way for enhanced clinical applications in Alzheimer's disease management.
METHODS: In this study, we utilized a combination of Mendelian randomization and advanced machine learning techniques to analyze transcriptomic data from five distinct cohorts of Alzheimer's Disease (AD) patients. After addressing batch effects, we identified differentially expressed genes (DEGs) between the AD and control groups. Mendelian randomization analysis was conducted to assess the causal relationships between DEGs and AD risk. A Venn diagram was subsequently used to identify genes associated with cholesterol metabolism from the screened gene set. The shared DEGs were subjected to functional enrichment analyses. Furthermore, immune analysis was quantified using Gene Set Enrichment Analysis (GSEA). A diagnostic model for AD was developed by evaluating 113 combinations of 12 machine learning algorithms with 10-fold cross-validation on the training datasets, followed by external validation on test datasets. Finally, immunofluorescence staining was performed on mouse brain slices to verify the expression level of KLHL21.
RESULTS: Our analyses identified a substantial number of differentially expressed genes (DEGs) demonstrating significant differences between Alzheimer's disease (AD) patients and control groups. Among these, we identified 29 genes associated with AD, with 21 of them linked to cholesterol metabolism, highlighting its pivotal role in the disease's pathogenesis. From this set, we developed a robust 8-gene diagnostic signature (comprising CHSY1, FIBP, DHCR24, HVCN1, KIFAP3, KLHL21, LETMD1, and SLC25A29), which outperformed existing AD diagnostic models in both training and testing cohorts. Additionally, complementary animal experiments were conducted to validate the biological relevance of these genes, further elucidating their roles in AD pathology.
CONCLUSIONS: Our research identified critical genes and proposed novel pathways for early diagnosis and potential therapeutic interventions, paving the way for enhanced clinical applications in Alzheimer's disease management.