Alzheimer's disease (AD) is a chronic neurodegenerative disease, and its underlying genes and treatments are unclear. Abnormalities in copper metabolism can prevent the clearance of β-amyloid peptides and promote the progression of AD pathogenesis. Therefore, the present study used a bioinformatics approach to perform an integrated analysis of the hub gene based on cuproptosis that can influence the diagnosis and treatment of AD. The gene expression profiles were obtained from the Gene Expression Omnibus database, including non-demented (ND) and AD samples. A total of 2,977 cuproptosis genes were retrieved from published articles. The seven hub genes associated with cuproptosis and AD were obtained from the differentially expressed genes and WGCNA in brain tissue from GSE33000. The GO analysis demonstrated that these genes were involved in phosphoribosyl pyrophosphate, lipid, and glucose metabolism. By stepwise regression and logistic regression analysis, we screened four of the seven cuproptosis genes to construct a diagnostic model for AD, which was validated by GES15222, GS48350, and GSE5281. In addition, immune cell infiltration of samples was investigated for correlation with these hub genes. We identified six drugs targeting these seven cuproptosis genes in DrugBank. Hence, these cuproptosis gene signatures may be an important prognostic indicator for AD and may offer new insights into treatment options.
Background: The pathophysiology of Alzheimer's disease (AD) involves the interplay of three different processes: pyroptosis, apoptosis, and necroptosis. However, the role of PANoptosis, a novel pro-inflammatory programmed cell death pathway, in AD remains unexplored.
Result: Our study utilized tissue expression profile data from AD patients to construct three distinct PANoptosis patterns, each with unique molecular and clinical characteristics. We have created a risk scoring system called the PANscore, which can analyze patterns specific to each AD patient. Additionally, we observed significantly lower levels of follicular helper T cells (Tfh) in the high PANscore and AD patients. Further analysis revealed a significant negative correlation of Tfh with GSDMD and MLKL.
Conclusion: These findings provide a roadmap for personalized patient stratification, enabling clinicians to develop personalized treatment plans for AD patients and advance the field of precision medicine.
Four types of A‐related RNA modification regulators interact with each other and even the crosstalk between the regulators could characterize the tumor immune microenvironment infiltration patterns, chemosensitivity, and cancer prognosis in patients with pan‐cancer.
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