AIMS

This study aims to explore the potential association between nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) in oligodendrocytes and Alzheimer's disease (AD), utilizing a combination of bioinformatics analysis and molecular biology experiments to validate this relationship.

METHODS

Public datasets related to AD were systematically retrieved and downloaded from the Gene Expression Omnibus (GEO) database at the National Center for Biotechnology Information (NCBI). Subsequently, the SVA package was employed to merge the data and eliminate batch effects, allowing for the precise identification of differentially expressed genes (DEGs) between AD patients and healthy controls. Advanced machine learning techniques, including LASSO regression analysis, random forest algorithms, and support vector machines (SVM), were utilized to analyze further the DEGs associated with the NLRP3 inflammasome to determine the gene set most closely related to AD. The effectiveness and clinical value of the gene-based diagnostic model were comprehensively assessed through receiver operating characteristic (ROC) curve analysis, nomogram construction, and decision curve analysis (DCA). Immune infiltration analysis evaluated the extent of various immune cell infiltrations in the brain tissue of AD patients. Single-cell transcriptomics and experiments were conducted to verify the molecular expression of NLRP3 in oligodendrocytes within the AD model.

RESULTS

A total of 11 significant DEGs were identified, with 4 genes showing downregulation and 7 genes exhibiting upregulation. All three algorithms-LASSO regression, random forest, and SVM-consistently identified PANX1, APP, P2RX7, MEFV, and NLRP3 as key genes closely associated with AD. ROC curve analysis, nomogram modeling, and DCA results demonstrated that the diagnostic model constructed based on these five genes exhibited high diagnostic accuracy and clinical applicability. Immune infiltration analysis revealed a significant correlation between key genes associated with AD and various immune cells, particularly CD8+ T cells, monocytes, activated NK cells, and neutrophils, suggesting that these cells may play important roles in the immunopathological process of AD. Single-cell transcriptomics indicated that the expression level of NLRP3 in oligodendrocytes was higher in the AD group compared to the control group (p < 0.05). Additionally, cell experiments using Reverse transcription quantitative PCR(RT-qPCR), immunofluorescence (IF), and Western blot (WB) analysis confirmed that the expression level of NLRP3 in oligodendrocytes was elevated in the AD model relative to the control group (p < 0.05).

CONCLUSION

This study corroborates the high expression of NLRP3 in AD and its close relationship with the disease through integrated bioinformatics analysis and molecular biology experiments. Furthermore, the diagnostic model constructed based on the five key genes-PANX1, APP, P2RX7, MEFV, and NLRP3-not only provides a robust tool for early diagnosis of AD but also offers new insights for the development of treatment targets for AD.