He Yue-E, Qiu Hui-Xian, Jiang Jian-Bing, Wu Rong-Zhou, Xiang Ru-Lian, Zhang Yuan-Hai
Department of Pediatric Cardiology, Children's Heart Center, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, Zhejiang 325027, P.R. China.
Mol Med Rep. 2017 Sep;16(3):2707-2713. doi: 10.3892/mmr.2017.6933. Epub 2017 Jul 6.
The aim of the present study was to identify key genes that may be involved in the pathogenesis of Tetralogy of Fallot (TOF) using bioinformatics methods. The GSE26125 microarray dataset, which includes cardiovascular tissue samples derived from 16 children with TOF and five healthy age‑matched control infants, was downloaded from the Gene Expression Omnibus database. Differential expression analysis was performed between TOF and control samples to identify differentially expressed genes (DEGs) using Student's t‑test, and the R/limma package, with a log2 fold‑change of >2 and a false discovery rate of <0.01 set as thresholds. The biological functions of DEGs were analyzed using the ToppGene database. The ReactomeFIViz application was used to construct functional interaction (FI) networks, and the genes in each module were subjected to pathway enrichment analysis. The iRegulon plugin was used to identify transcription factors predicted to regulate the DEGs in the FI network, and the gene‑transcription factor pairs were then visualized using Cytoscape software. A total of 878 DEGs were identified, including 848 upregulated genes and 30 downregulated genes. The gene FI network contained seven function modules, which were all comprised of upregulated genes. Genes enriched in Module 1 were enriched in the following three neurological disorder‑associated signaling pathways: Parkinson's disease, Alzheimer's disease and Huntington's disease. Genes in Modules 0, 3 and 5 were dominantly enriched in pathways associated with ribosomes and protein translation. The Xbox binding protein 1 transcription factor was demonstrated to be involved in the regulation of genes encoding the subunits of cytoplasmic and mitochondrial ribosomes, as well as genes involved in neurodegenerative disorders. Therefore, dysfunction of genes involved in signaling pathways associated with neurodegenerative disorders, ribosome function and protein translation may contribute to the pathogenesis of TOF.
本研究的目的是使用生物信息学方法鉴定可能参与法洛四联症(TOF)发病机制的关键基因。从基因表达综合数据库下载了GSE26125微阵列数据集,该数据集包括来自16名TOF患儿和5名年龄匹配的健康对照婴儿的心血管组织样本。使用学生t检验和R/limma软件包对TOF样本和对照样本进行差异表达分析,以鉴定差异表达基因(DEG),将log2倍数变化>2和错误发现率<0.01设定为阈值。使用ToppGene数据库分析DEG的生物学功能。使用ReactomeFIViz应用程序构建功能相互作用(FI)网络,并对每个模块中的基因进行通路富集分析。使用iRegulon插件鉴定预测可调节FI网络中DEG的转录因子,然后使用Cytoscape软件可视化基因-转录因子对。共鉴定出878个DEG,包括848个上调基因和30个下调基因。基因FI网络包含七个功能模块,均由上调基因组成。模块1中富集的基因在以下三个与神经疾病相关的信号通路中富集:帕金森病、阿尔茨海默病和亨廷顿病。模块0、3和5中的基因主要富集在与核糖体和蛋白质翻译相关的通路中。已证明X盒结合蛋白1转录因子参与调节编码细胞质和线粒体核糖体亚基的基因,以及参与神经退行性疾病的基因。因此,参与神经退行性疾病相关信号通路、核糖体功能和蛋白质翻译的基因功能障碍可能有助于TOF的发病机制。