Imagine a dance party where different dancers with unique moves come together to form clusters on the dance floor. In a similar fashion, our study delved into the world of cuproptosis-related molecular clusters in Alzheimer’s disease. We wanted to understand how cuproptosis, a type of cell death, influences the progression of Alzheimer’s disease. Using a large dataset of Alzheimer’s disease samples, we analyzed gene expression profiles and immune characteristics to identify these molecular clusters. The clusters revealed distinct patterns of immune infiltration, indicating varied immune responses within each cluster. With our findings, we constructed a prediction model that accurately assesses the risk of cuproptosis subtypes and predicts the pathological outcome in Alzheimer’s disease patients. By identifying five model-related genes associated with Aβ-42 levels and β-secretase activity, we gained further insights into the mechanism underlying Alzheimer’s disease. This research offers a comprehensive understanding of the intricate relationship between cuproptosis and Alzheimer’s disease, paving the way for future therapeutic strategies. Discover more by exploring the full article!

IntroductionAlzheimer’s disease is the most common dementia with clinical and pathological heterogeneity. Cuproptosis is a recently reported form of cell death, which appears to result in the progression of various diseases. Therefore, our study aimed to explore cuproptosis-related molecular clusters in Alzheimer’s disease and construct a prediction model.MethodsBased on the GSE33000 dataset, we analyzed the expression profiles of cuproptosis regulators and immune characteristics in Alzheimer’s disease. Using 310 Alzheimer’s disease samples, we explored the molecular clusters based on cuproptosis-related genes, along with the related immune cell infiltration. Cluster-specific differentially expressed genes were identified using the WGCNA algorithm. Subsequently, the optimal machine model was chosen by comparing the performance of the random forest model, support vector machine model, generalized linear model, and eXtreme Gradient Boosting. Nomogram, calibration curve, decision curve analysis, and three external datasets were applied for validating the predictive efficiency.ResultsThe dysregulated cuproptosis-related genes and activated immune responses were determined between Alzheimer’s disease and non-Alzheimer’s disease controls. Two cuproptosis-related molecular clusters were defined in Alzheimer’s disease. Analysis of immune infiltration suggested the significant heterogeneity of immunity between distinct clusters. Cluster2 was characterized by elevated immune scores and relatively higher levels of immune infiltration. Functional analysis showed that cluster-specific differentially expressed genes in Cluster2 were closely related to various immune responses. The Random forest machine model presented the best discriminative performance with relatively lower residual and root mean square error, and a higher area under the curve (AUC = 0.9829). A final 5-gene-based random forest model was constructed, exhibiting satisfactory performance in two external validation datasets (AUC = 0.8529 and 0.8333). The nomogram, calibration curve, and decision curve analysis also demonstrated the accuracy to predict Alzheimer’s disease subtypes. Further analysis revealed that these five model-related genes were significantly associated with the Aβ-42 levels and β-secretase activity.ConclusionOur study systematically illustrated the complicated relationship between cuproptosis and Alzheimer’s disease, and developed a promising prediction model to evaluate the risk of cuproptosis subtypes and the pathological outcome of Alzheimer’s disease patients.

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