Chen Xi, Wang Qiao-Ling, Zhang Meng-Hui
Department of Ophthalmology, The Ninth Hospital of Chongqing, Chongqing 400700, P.R. China.
Department of Ophthalmology, The Second Hospital of Jinan, Jinan, Shandong 250022, P.R. China.
Exp Ther Med. 2017 Oct;14(4):3651-3657. doi: 10.3892/etm.2017.4931. Epub 2017 Aug 16.
The current study aimed to identify key genes in glaucoma based on a benchmarked dataset and gene regulatory network (GRN). Local and global noise was added to the gene expression dataset to produce a benchmarked dataset. Differentially-expressed genes (DEGs) between patients with glaucoma and normal controls were identified utilizing the Linear Models for Microarray Data (Limma) package based on benchmarked dataset. A total of 5 GRN inference methods, including Zscore, GeneNet, context likelihood of relatedness (CLR) algorithm, Partial Correlation coefficient with Information Theory (PCIT) and GEne Network Inference with Ensemble of Trees (Genie3) were evaluated using receiver operating characteristic (ROC) and precision and recall (PR) curves. The interference method with the best performance was selected to construct the GRN. Subsequently, topological centrality (degree, closeness and betweenness) was conducted to identify key genes in the GRN of glaucoma. Finally, the key genes were validated by performing reverse transcription-quantitative polymerase chain reaction (RT-qPCR). A total of 176 DEGs were detected from the benchmarked dataset. The ROC and PR curves of the 5 methods were analyzed and it was determined that Genie3 had a clear advantage over the other methods; thus, Genie3 was used to construct the GRN. Following topological centrality analysis, 14 key genes for glaucoma were identified, including , and and 5 of these 14 key genes were validated by RT-qPCR. Therefore, the current study identified 14 key genes in glaucoma, which may be potential biomarkers to use in the diagnosis of glaucoma and aid in identifying the molecular mechanism of this disease.
当前的研究旨在基于一个基准数据集和基因调控网络（GRN）来识别青光眼的关键基因。向基因表达数据集中添加局部和全局噪声以生成一个基准数据集。基于基准数据集，利用微阵列数据线性模型（Limma）软件包识别青光眼患者和正常对照之间的差异表达基因（DEG）。使用包括Zscore、GeneNet、相关性上下文似然度（CLR）算法、信息理论偏相关系数（PCIT）和树集成基因网络推断（Genie3）在内的5种GRN推断方法，通过受试者工作特征（ROC）曲线以及精确率和召回率（PR）曲线进行评估。选择性能最佳的推断方法来构建GRN。随后，进行拓扑中心性分析（度、接近度和中间中心性）以识别青光眼GRN中的关键基因。最后，通过进行逆转录定量聚合酶链反应（RT-qPCR）对关键基因进行验证。从基准数据集中共检测到176个DEG。分析了这5种方法的ROC和PR曲线，确定Genie3比其他方法具有明显优势；因此，使用Genie3构建GRN。经过拓扑中心性分析，确定了14个青光眼关键基因，包括……，并且这14个关键基因中的5个通过RT-qPCR得到验证。因此，当前研究确定了14个青光眼关键基因，它们可能是用于青光眼诊断的潜在生物标志物，并有助于确定该疾病的分子机制。
