Alzheimer's disease (AD) is the main cause of dementia in older adults. Recently, increasing evidence shows that PANoptosis plays an important role in AD.
Objective
This study investigated potential roles of PANoptosis by bioinformatics and machine learning in AD.
Methods
AD-related microarray sets were downloaded from the GEO database and PANoptosis-related genes were extracted from the GeneCards database. By WGCNA and constructing machine learning models, hub genes were identified and verified. A ceRNA network was established using cytoscape. The ssGSEA was used to estimate immune cell infiltration and its correlation with hub genes. The R package was performed for consensus clustering (CC) analysis.
Results
240 differentially expressed genes in the training set were identified. By selecting optimal models, we finally identified five PANoptosis-related hub genes in AD: ADCYAP1, BCL6, CXCR4, SPP1, and PGF, which were verified in the validation set (excluding SPP1 unverified) and the Aβ25–35-induced AD cell model. Subsequently, a risk prediction model with good performance for AD and a ceRNA network was established. Then, it was found that 14 types of immune cells with increased expression and 5 types with decreased expression in AD, significantly related to hub genes. Finally, two AD subtypes were proposed based on CC analysis: high immune infiltrative (more immune cell expression associated with inflammation and programmed cell death pathways) and low immune infiltrative subtype.
Conclusions
Our results suggest that five PANoptosis-related genes are significantly associated with the pathologic progression of AD; we proposed two AD immune infiltrative subtypes.
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