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首页> 外文期刊>European review for medical and pharmacological sciences. >Bioinformatic analysis of microarray data reveals several key genes related to heart failure
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Bioinformatic analysis of microarray data reveals several key genes related to heart failure

机译:微阵列数据的生物信息学分析揭示了与心力衰竭相关的几个关键基因

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OBJECTIVES: Heart failure is a major public health problem worldwide. However, the molecular mechanism is still unclear. This study aims to discover differentially expressed genes (DEGs) between non-ischemic or ischemic heart failure samples and healthy control, which may be used for diagnosis and treatment of heart failure. MATERIALS AND METHODS: Gene expression profile GSE9128 was downloaded from Gene Expression Omnibus, including 3 normal samples, 4 non-ischemic heart failure samples and 4 ischemic samples. Data processing and differential analysis were carried out with packages of R. Cluster analysis was also performed for all the samples to globally observe the difference among the three groups of samples. Interactors of the DEGs were retrieved with Osprey and then networks were constructed. The overlapping part of the network was selected out using Cytoscape, for which functional enrichment analysis was applied with DAVID tools. RESULTS: A total of 293 and 133 DEGs were obtained for non-ischemic and ischemic heart failure, respectively. Two networks were established and then functional enrichment analysis revealed that “regulation of programmed cell death” was most significantly over-represented in common DEGs. CONCLUSIONS: Genes differentially expressed in non-ischemic and ischemic heart failure can be biomarkers to distinguish the two types of heart failure. Besides, these genes can be targets to develop treatments.
机译:目的:心力衰竭是世界范围内的主要公共卫生问题。但是,分子机制仍不清楚。本研究旨在发现非缺血性或缺血性心力衰竭样本与健康对照之间的差异表达基因(DEG),这些基因可用于心力衰竭的诊断和治疗。材料与方法:基因表达谱GSE9128从Gene Expression Omnibus下载,包括3个正常样品,4个非缺血性心力衰竭样品和4个缺血性样品。使用R包进行数据处理和差异分析。还对所有样品进行了聚类分析,以全局观察三组样品之间的差异。用Osprey检索DEG的交互器,然后构建网络。使用Cytoscape选择了网络的重叠部分,并使用DAVID工具对其进行了功能富集分析。结果:非缺血性和缺血性心力衰竭分别获得293和133个DEGs。建立了两个网络,然后功能富集分析表明,“程序性细胞死亡的调控”在常见的DEGs中最明显。结论:在非缺血性和缺血性心力衰竭中差异表达的基因可以作为区分两种类型心力衰竭的生物标志物。此外,这些基因可以作为开发治疗的靶标。

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