基于多转录组和机器学习的阿尔茨海默病焦热相关基因的鉴定和验证。

IF 4.1 2区 医学 Q2 GERIATRICS & GERONTOLOGY
Frontiers in Aging Neuroscience Pub Date : 2025-05-14 eCollection Date: 2025-01-01 DOI:10.3389/fnagi.2025.1568337
Yuntai Wang, Yilin Li, Lu Zhou, Yihuan Yuan, Chuanfei Liu, Zimeng Zeng, Yuanqi Chen, Qi He, Zhuoze Wu
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引用次数: 0

摘要

背景:阿尔茨海默病(AD)的进展以持续的神经炎症为特征,其中焦热-一种炎症性程序性细胞死亡机制-已成为关键的病理因素。然而,热释相关基因(PRGs)驱动AD发病机制的分子机制尚未完全阐明。方法:我们整合GEO数据库中AD患者的多个转录组,并在组合数据集中分析PRGs的表达。采用机器学习算法和综合生物信息学分析(包括免疫浸润和接受者工作特征(ROC))来识别中心基因。此外,我们利用AD小鼠的表达数据验证了这些关键基因的表达模式,并通过时间序列和相关分析构建了潜在的调控网络。结果:通过加权基因共表达网络分析(WGCNA)和差异表达基因分析,我们鉴定出91个AD PRGs。应用蛋白-蛋白相互作用和机器学习算法,鉴定出7个焦亡特征基因(CHMP2A、EGFR、FOXP3、HSP90B1、MDH1、METTL3和PKN2)。至关重要的是,MDH1和PKN2在免疫细胞浸润、ROC曲线和实验验证方面表现出优越的性能。此外,我们利用不同年龄AD小鼠的基因表达谱构建了这些特征基因的长链非编码RNA和mRNA (lncRNA-mRNA)调控网络,揭示了AD中潜在的调控机制。结论:本研究首次全面表征了AD中与焦热相关的分子特征。鉴定出7个枢纽基因,特别强调MDH1和PKN2。通过对患者和小鼠转录组的综合生物信息学分析以及实验数据,验证了其优越的性能。我们的研究结果为阿尔茨海默病的焦亡机制建立了基础见解,可能为针对神经炎症途径的新治疗策略提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identification and validation of pyroptosis-related genes in Alzheimer's disease based on multi-transcriptome and machine learning.

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.

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来源期刊
Frontiers in Aging Neuroscience
Frontiers in Aging Neuroscience GERIATRICS & GERONTOLOGY-NEUROSCIENCES
CiteScore
6.30
自引率
8.30%
发文量
1426
期刊介绍: Frontiers in Aging Neuroscience is a leading journal in its field, publishing rigorously peer-reviewed research that advances our understanding of the mechanisms of Central Nervous System aging and age-related neural diseases. Specialty Chief Editor Thomas Wisniewski at the New York University School of Medicine is supported by an outstanding Editorial Board of international researchers. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics, clinicians and the public worldwide.
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