论文部分内容阅读
结核病(tuberculosis,TB)对人类具有非常大的威胁,感染过结核病的人占世界总人口数的三分之一,而且每年有造成超过一百万人的死亡。为了寻找可用于结核病诊断和治疗的分子标志物,我们从微阵列数据存储中心基因表达综合数据库(gene expression omnibus,GEO)中下载了原始数据,将来源于结核病患者的外周血单核细胞与健康人的基因进行了比较,共分析筛选出310个差异共表达的基因。随后,我们应用DAVID(the database for annotation,visualization and integrated discovery)数据库对这些差异共表达基因进行了GO(gene ontology)功能富集分析和KEGG(kyoto encyclopedia of genes and genomes)通路分析。通过蛋白互作网络,我们找到了CCL20、JAK2、STAT1和IL-1β4个结核病的关键基因。我们的研究表明,数据挖掘和整合能够成为研究结核病诊断标志物及其发生发展机制的有用工具,并可为结核病的诊断和治疗带来新思路。
Tuberculosis (TB) is a very large human threat. One third of the world’s population is infected with tuberculosis and more than one million people die each year. To search for molecular markers for diagnosis and treatment of tuberculosis, we downloaded the original data from the microarray data storage center gene expression omnibus (GEO), comparing peripheral blood mononuclear cells from healthy people with tuberculosis Human genes were compared, a total of 310 differentially co-analyzed and screened genes. Subsequently, we used the DAVID (the database for annotation, visualization and integrated discovery) database to conduct GO (gene ontology) enrichment analysis and KEGG (kyoto encyclopedia of genes and genomes) pathway analysis. Through the protein interaction network, we found 4 key genes of CCL20, JAK2, STAT1 and IL-1β tuberculosis. Our research shows that data mining and integration can be useful tools for studying diagnostic markers of TB and their mechanisms of development and development and can bring new ideas for the diagnosis and treatment of tuberculosis.