Identification of immune infiltration and PANoptosis-related molecular clusters and predictive model in Alzheimer’s disease based on transcriptome analysis

Abstract

This study aims to explore the expression profile of PANoptosis-related genes (PRGs) and immune infiltration in Alzheimer’s disease (AD). Based on the Gene Expression Omnibus database, this study investigated the differentially expressed PRGs and immune cell infiltration in AD and explored related molecular clusters. Gene set variation analysis (GSVA) was used to analyze the expression of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes in different clusters. Weighted gene co-expression network analysis was utilized to find co-expressed gene modules and core genes in the network. By analyzing the intersection genes in random forest, support vector machine, generalized linear model, and extreme gradient boosting (XGB), the XGB model was determined. Eventually, the first five genes (Signal Transducer and Activator of Transcription 3, Tumor Necrosis Factor (TNF) Receptor Super-family Member 1B, Interleukin 4 Receptor, Chloride Intracellular Channel 1, TNF Receptor Superfamily Member 10B) in XGB model were selected as predictive genes. This research explored the relationship between PANoptosis and AD and established an XGB learning model to evaluate and screen key genes. At the same time, immune infiltration analysis showed that there were different immune infiltration expression profiles in AD.

Keywords

Alzheimer’s disease / immune infiltration / machine-learning model / molecular clusters / PANoptosis

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