A 3-Gene-Based Diagnostic Signature in Alzheimer's Disease
A 3-Gene-Based Diagnostic Signature in Alzheimer's Disease
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DOI：
10.1159/000518727
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发表时间：
2021-09-14
影响因子：
2.4
通讯作者：
Gao, Sheng
中科院分区：
文献类型：
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作者：
Wang, Huimin;Zhang, Yanqiu;Gao, Sheng
背景：阿尔茨海默病（AD）是一种慢性神经退行性疾病。在这项研究中，确定了AD的潜在诊断生物标志物。研究方法：从GEO数据库中的2个数据集中收集AD患者和健康人群的基因组数据，利用R语言的limma软件包对其中的差异表达基因进行分析。利用R.使用STRING数据库和Cytoscape进行PPI网络构建和基因预测。然后，构建逻辑回归模型来预测样本类型。结果如下：对GEO数据集进行生物信息学分析，发现GSE 5281和GSE 4226数据集中分别有2,063个和108个DEG，并发现15个DEG重叠，GO和KEGG富集分析发现与神经发育相关的术语。然后，我们基于PPI网络中的枢纽基因构建了逻辑回归模型，并将模型优化为3个基因（ALDOA，ENC 1和NFKBIA）。训练集GSE 5281和测试集GSE 4226的曲线下面积值分别为0.9647和0.7857，这表明该模型的有效性。结论：本研究对AD患者基因表达进行了全面的生物信息学分析，并建立了有效的logistic回归模型，为AD的诊断方法提供了有前景的研究价值。
Background: Alzheimer's disease (AD) is a chronic neurodegenerative disease. In this study, potential diagnostic biomarkers were identified for AD. Methods: All AD samples and healthy samples were collected from 2 datasets in the GEO database, in which differentially expressed genes (DEGs) were analyzed by using the limma package of R language. GO and KEGG pathway enrichment was conducted basing on the DEGs via the clusterProfiler package of R. And, the PPI network construction and gene prediction were performed using the STRING database and Cytoscape. Then, a logistic regression model was constructed to predict the sample type. Results: Bioinformatic analysis of GEO datasets revealed 2,063 and 108 DEGs in GSE5281 and GSE4226 datasets, separately, and 15 overlapping DEGs were found. GO and KEGG enrichment analysis revealed terms associated with neurodevelopment. Then, we built a logistic regression model based on the hub genes from the PPI network and optimized the model to 3 genes (ALDOA, ENC1, and NFKBIA). The values of area under the curve of the training set GSE5281 and testing set GSE4226 were 0.9647 and 0.7857, respectively, which implied the efficacy of this model. Conclusion: The comprehensive bioinformatic analysis of gene expression in AD patients and the effective logistic regression model built in our study may provide promising research value for diagnostic methods of AD.