Current Alzheimer Research - Volume 22, Issue 1, 2025

Alzheimer’s disease (AD) represents the most common neurodegenerative disorder, characterized by progressive cognitive decline and memory loss. Despite the recognition of mitochondrial dysfunction as a critical factor in the pathogenesis of AD, the specific molecular mechanisms remain largely undefined.

This study aimed to identify novel biomarkers and therapeutic strategies associated with mitochondrial dysfunction in AD by employing bioinformatics combined with machine learning methodologies. We performed Weighted Gene Co-expression Network Analysis (WGCNA) utilizing gene expression data from the NCBI Gene Expression Omnibus (GEO) database and isolated mitochondria-related genes through the MitoCarta3.0 database. By intersecting WGCNA-derived module genes with identified mitochondrial genes, we compiled a list of 60 mitochondrial dysfunction-related genes (MRGs) significantly enriched in pathways pertinent to mitochondrial function, such as the citrate cycle and oxidative phosphorylation.

Employing machine learning techniques, including random forest and LASSO, along with the CytoHubba algorithm, we identified key genes with strong diagnostic potential, such as ACO2, CS, MRPS27, SDHA, SLC25A20, and SYNJ2BP, verified through ROC analysis. Furthermore, an interaction network involving miRNA-MRGs-transcription factors and a protein-drug interaction network revealed potential therapeutic compounds such as Congo red and kynurenic acid that target MRGs.

These findings delineate the intricate role of mitochondrial dysfunction in AD and highlight promising avenues for further exploration of biomarkers and therapeutic interventions in this devastating disease.

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.

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 in vitro experiments were conducted to verify the molecular expression of NLRP3 in oligodendrocytes within the AD model.

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, in vitro 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).

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.