Dysregulated interleukin networks drive immune heterogeneity in Alzheimer's disease: an immunogenomic approach to subgroup classification and predictive modeling

白细胞介素网络失调驱动阿尔茨海默病免疫异质性:基于免疫基因组学的亚组分类和预测建模方法

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Abstract

BACKGROUND: Alzheimer's Disease (AD) is marked by intricate immunological alterations, including the dysregulation of interleukin signaling. This study investigates the differential expression and potential roles of interleukins and their receptors in AD patients. METHODS: We analyzed the GSE48350 dataset to assess the single-sample Gene Set Enrichment Analysis (ssGSEA) scores for interleukins and their receptors between normal and AD groups. Differentially expressed interleukin-related genes (DIGs) were identified. Enrichment analysis was conducted to understand functional implications. LASSO and logistic regression were used to identify key interleukin genes, which were employed to construct a predictive nomogram. This model was validated using the GSE132903 dataset. Unsupervised clustering and immune cell infiltration analyses were performed to examine AD patient heterogeneity. RESULTS: The ssGSEA scores indicated significantly elevated interleukin and receptor levels in AD patients. A total of 23 DIGs were discovered, and the enrichment analysis emphasized their participation in immune signaling pathways. The nomogram based on key interleukin genes demonstrated strong predictive capability, with an AUC of 0.882 in the training set and 0.837 in the validation set. Unsupervised clustering revealed two AD subgroups with distinct immune profiles and pathway activities. Subgroup C2 exhibited higher immune cell infiltration and pathway activity than subgroup C1. CONCLUSION: Interleukins and their receptors are significantly upregulated in AD patients, with distinct immune profiles identified in AD subgroups. The predictive nomogram effectively stratifies AD patients based on interleukin gene expression. These findings provide insights into AD's immunological landscape and suggest potential biomarkers for personalized therapeutic strategies.

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