BACKGROUND: Alzheimer's disease (AD) progression is characterized by persistent neuroinflammation, where pyroptosis-an inflammatory programmed cell death mechanism-has emerged as a key pathological contributor. However, the molecular mechanisms through which pyroptosis-related genes (PRGs) drive AD pathogenesis remain incompletely elucidated. METHODS: We integrated multiple transcriptomes of AD patients from the GEO database and analyzed the expression of PRGs in combined datasets. Machine learning algorithms and comprehensive bioinformatics analysis (including immune infiltration and receiver operating characteristic (ROC)) were applied to identify the hub genes. Additionally, we validated the expression patterns of these key genes using the expression data from AD mice and constructed potential regulatory networks through time series and correlation analysis. RESULTS: We identified 91 PRGs in AD using the weighted gene co-expression network analysis (WGCNA) and differentially expressed genes analysis. By application of the protein-protein interaction and machine learning algorithms, seven pyroptosis feature genes (CHMP2A, EGFR, FOXP3, HSP90B1, MDH1, METTL3, and PKN2) were identified. Crucially, MDH1 and PKN2 demonstrated superior performance in terms of immune cell infiltration, ROC curves, and experimental validation. Furthermore, we constructed the long non-coding RNA and mRNA (lncRNA-mRNA) regulatory network of these characteristic genes using the gene expression profiles from AD mice at varying ages, revealing the potential regulatory mechanism in AD. CONCLUSION: This study provides the first comprehensive characterization of pyroptosis-related molecular signatures in AD. Seven hub genes were identified, with particular emphasis on MDH1 and PKN2. Their superior performances were validated through comprehensive bioinformatic analysis in both patient and mouse transcriptomes, as well as the experimental data. Our findings establish foundational insights into pyroptosis mechanisms in AD that may inform novel treatment strategies targeting neuroinflammatory pathways.
Identification and validation of pyroptosis-related genes in Alzheimer's disease based on multi-transcriptome and machine learning.
基于多转录组和机器学习的阿尔茨海默病中细胞焦亡相关基因的鉴定和验证
阅读:11
作者:Wang Yuntai, Li Yilin, Zhou Lu, Yuan Yihuan, Liu Chuanfei, Zeng Zimeng, Chen Yuanqi, He Qi, Wu Zhuoze
| 期刊: | Frontiers in Aging Neuroscience | 影响因子: | 4.500 |
| 时间: | 2025 | 起止号: | 2025 May 14; 17:1568337 |
| doi: | 10.3389/fnagi.2025.1568337 | 研究方向: | 细胞生物学 |
特别声明
1、本文转载旨在传播信息,不代表本网站观点,亦不对其内容的真实性承担责任。
2、其他媒体、网站或个人若从本网站转载使用,必须保留本网站注明的“来源”,并自行承担包括版权在内的相关法律责任。
3、如作者不希望本文被转载,或需洽谈转载稿费等事宜,请及时与本网站联系。
4、此外,如需投稿,也可通过邮箱info@biocloudy.com与我们取得联系。
