Exploring the molecular characterization of PANoptosis-related genes with features of immune dysregulation in Alzheimer's disease based on bulk and single-nuclei RNA sequencing.

The immune system has emerged as a major factor in the pathogenesis of Alzheimer's disease (AD). PANoptosis is a newly defined programmed cell death mechanism related to many inflammatory diseases. This study aimed to identify the differentially expressed (DE) PANoptosis-related genes with characteristics of immune dysregulation (PRGIDs) in AD using bioinformatics analysis of bulk RNA-seq and single-nuclei RNA sequencing (snRNA-seq) data. To improve the robustness of gene selection, we integrated 3 microarray and 6 snRNA-seq datasets from the Gene Expression Omnibus (GEO), which allowed us to not only examine overall gene expression patterns but also assess the cellular specificity of gene expression at the single-cell level. This approach helped to identify cell-type-specific gene alterations that may be masked in bulk RNA-seq analyses. Relevant PANoptosis, immune dysregulation, and AD-related genes were obtained from the Genecards database. The AlzData database was also used in this study. Expression validation, the least absolute shrinkage and selection operator (LASSO) regression model, and CytoHubba algorithms were applied for key DE-PRGIDs selection. LASSO, Logistic, and Cox regressions were used to construct prognostic models. The receiver operating characteristic (ROC) curve and correlation analyses were conducted on key DE-PRGIDs. The Seurat package in R software was employed for performing snRNA-seq data processing. Uniform manifold approximation and projection (UMAP) was utilized for cell type annotation and PRGID cell visualization. The violin plot was applied for displaying expression levels of PRGIDs. High-dimensional consensus weighted gene co-expression network analysis (hdWGCNA) was conducted on microglia to identify gene modules and hub genes. Venn diagram analysis identified 250 PRGIDs and 39 DE-PRGIDs. NFKBIA was identified as the key gene. Prognostic models based on the expression level of NFKBIA were obtained. ROC curve analysis revealed its area under the curve (AUC) value: 0.661 in training set and 0.836 in validation set. The heatmap displayed the result of correlation analysis. SnRNA-seq data analysis identified 7 cell types. The UMAP and violin plots revealed highly expressed PRGIDs in microglia with remarkable differences between healthy controls and AD. hdWGCNA identified PVT1 and APOE as hub genes associated with microglia. In conclusion, our findings provide evidence that PANoptosis may play a role in the immune dysregulation observed in AD. PVT1 has been implicated in AD pathogenesis, potentially exerting its effects through the miR-488-3p/FOXD3/SCN2A axis.