Unveiling Immune-related feature genes for Alzheimer’s disease based on machine learning - Zhang, Guimei; Sun, Shuo; Wang, Yingying; Zhao, Yang; Sun, Li (Frontiers in Immunology, 2024)
The identification of diagnostic and therapeutic biomarkers for Alzheimer's Disease (AD) remains a crucial area of research. In this study, utilizing the Weighted Gene Co-expression Network Analysis (WGCNA) algorithm, we identified RHBDF2 and TNFRSF10B as key feature genes associated with AD pathogenesis. Analyzing data from the GSE33000 dataset, we revealed significant upregulation of RHBDF2 and TNFRSF10B in AD patients, with correlations to age and gender. Interestingly, their expression profile in AD differs notably from that of other neurodegenerative conditions. Functional analysis unveiled their involvement in immune response and various signaling pathways implicated in AD pathogenesis. Furthermore, our study demonstrated the potential of RHBDF2 and TNFRSF10B as diagnostic biomarkers, exhibiting high discrimination power in distinguishing AD from control samples. External validation across multiple datasets confirmed the robustness of the diagnostic model. Moreover, utilizing molecular docking analysis, we identified dinaciclib and tanespimycin as promising small molecule drugs targeting RHBDF2 and TNFRSF10B for potential AD treatment. Our findings highlight the diagnostic and therapeutic potential of RHBDF2 and TNFRSF10B in AD management, shedding light on novel strategies for precision medicine in AD. visuospatial, language, and executive functions (1). Data from the World Alzheimer's Disease Report 2023 suggests that the number of people with dementia worldwide will increase from 55 million in 2019 to 139 million in 2050 (2). Moreover, with rapidly aging populations throughout, the number of dementia patients will further increase. As the leading cause of dementia (accounting for 60-80% of all cases), AD prevalence is also on the rise, posing an ever greater socioeconomic and healthcare burden.Genetic predisposition has been established as a major risk factor for AD. Mutations in several genes, including amyloid precursor protein, presenilin 1, presenilin 2, and the ε4 allele of apolipoprotein E, have been implicated in AD pathogenesis (3). Further, a number of immune-related genes have been identified as strongly associated with AD (4). Genome-wide association studies