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通过生物信息学和机器学习对阿尔茨海默病核心基因进行鉴定与实验验证。

Identification and experimental validation of Alzheimer's disease hub genes via bioinformatics and machine learning.

作者信息

Hu Ying, Pan Zhaoshuyu, Li Jianping, Tang Bin, Luo Mingbo, Li Yu, Cao Xue, Zheng Kaiwen, Wang Nana, Xu Chuanjie

机构信息

Department of Critical Care Medicine of the Third Affiliated Hospital (The First People's Hospital of Zunyi), Zunyi Medical University Guizhou Province, Zunyi, China.

出版信息

J Alzheimers Dis Rep. 2025 Jul 15;9:25424823251356300. doi: 10.1177/25424823251356300. eCollection 2025 Jan-Dec.

DOI:10.1177/25424823251356300
PMID:40678591
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12267949/
Abstract

BACKGROUND

Alzheimer's disease (AD) is a complex neurodegenerative disorder.

OBJECTIVE

To identify diagnostic and predictive biomarkers for AD.

METHODS

Based on three GEO datasets of human brain tissue from AD patients and controls, weighted gene co-expression network analysis (WGCNA) and enrichment analysis were used to identify AD-related gene modules. Hub genes were screened via protein-protein interaction (PPI) analysis and three machine learning algorithms. Diagnostic efficacy was evaluated using receiver operating characteristic (ROC) curves. Immune cell infiltration and hub gene expression correlations were analyzed using xCell. In vivo validation was performed using an AD mouse model.

RESULTS

The magenta module was significantly correlated with AD. PPI network analysis identified 15 AD-related genes, mainly enriched in mitochondria and ribosomes. Two hub genes, DLAT and CCDC88b, were identified. DLAT was significantly downregulated in AD, and CCDC88b was upregulated (p < 0.01); both findings were validated via qPCR in AD model mice. ROC analysis showed good diagnostic performance. Immune infiltration analysis revealed macrophages as the dominant cell type, with hub gene expression associated with immune cell presence.

CONCLUSIONS

DLAT and CCDC88b are potential novel biomarkers for AD and may serve as targets for therapeutic intervention.

摘要

背景

阿尔茨海默病（AD）是一种复杂的神经退行性疾病。

目的

识别AD的诊断和预测生物标志物。

方法

基于来自AD患者和对照的三个人脑组织GEO数据集，使用加权基因共表达网络分析（WGCNA）和富集分析来识别与AD相关的基因模块。通过蛋白质-蛋白质相互作用（PPI）分析和三种机器学习算法筛选枢纽基因。使用受试者工作特征（ROC）曲线评估诊断效能。使用xCell分析免疫细胞浸润与枢纽基因表达的相关性。使用AD小鼠模型进行体内验证。

结果

品红色模块与AD显著相关。PPI网络分析确定了15个与AD相关的基因，主要富集于线粒体和核糖体。确定了两个枢纽基因，即DLAT和CCDC88b。DLAT在AD中显著下调，CCDC88b上调（p < 0.01）；这两个发现均在AD模型小鼠中通过qPCR得到验证。ROC分析显示出良好的诊断性能。免疫浸润分析显示巨噬细胞是主要细胞类型，枢纽基因表达与免疫细胞存在相关。

结论

DLAT和CCDC88b是AD潜在的新型生物标志物，可能作为治疗干预的靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6ad/12267949/5c1cfcdabf47/10.1177_25424823251356300-fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6ad/12267949/e5e1c4bdb8e3/10.1177_25424823251356300-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6ad/12267949/f0bddc3db490/10.1177_25424823251356300-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6ad/12267949/5472af68fd4a/10.1177_25424823251356300-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6ad/12267949/7ed98cfd743f/10.1177_25424823251356300-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6ad/12267949/e21d60f34102/10.1177_25424823251356300-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6ad/12267949/551a8ab0d304/10.1177_25424823251356300-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6ad/12267949/6f25cf9640cb/10.1177_25424823251356300-fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6ad/12267949/5c1cfcdabf47/10.1177_25424823251356300-fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6ad/12267949/e5e1c4bdb8e3/10.1177_25424823251356300-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6ad/12267949/f0bddc3db490/10.1177_25424823251356300-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6ad/12267949/5472af68fd4a/10.1177_25424823251356300-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6ad/12267949/7ed98cfd743f/10.1177_25424823251356300-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6ad/12267949/e21d60f34102/10.1177_25424823251356300-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6ad/12267949/551a8ab0d304/10.1177_25424823251356300-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6ad/12267949/6f25cf9640cb/10.1177_25424823251356300-fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d6ad/12267949/5c1cfcdabf47/10.1177_25424823251356300-fig8.jpg

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