生物信息学筛选活动性肺结核的潜在生物标志物的临床研究

Screening Potential Biomarkers of Active Pulmonary Tuberculosis by Bioinformatics

目的:利用生物信息学技术筛选活动性肺结核(PTB)诊断和治疗监测的潜在生物标志物。方法:通过GEO数据库下载GSE19444和GSE19435基因芯片数据集。利用GEO2R工具筛选活动性PTB患者与健康对照间的差异表达基因(DEG),使用CentiScape插件确定中枢基因(Hub基因),并通过GSE56153数据集验证Hub基因在活动性PTB治疗期间的变化。结果:两个数据集中共筛选505个DEG,上调251个,下调254个。DEG网络的9个子模块中模块1评分最高,包含的25个基因主要参与病毒防御、干扰素信号通路等反应。在25个基因中筛选了13个Hub基因,通过验证,提示上调的IFIT3、IFI35、STAT1和GBP1在初诊活动性PTB、治疗8周、治疗28周和健康对照组的表达水平有显著降低趋势。结论:筛选出的IFIT3、IFI35、STAT1和GBP1与活动性PTB的发生和治疗密切相关,有望成为PTB诊断和治疗监测的潜在生物标志物。

Objective To screen potential biomarkers for diagnosis and treatment monitoring of active pulmonary tuberculosis(PTB)by bioinformatics.Methods GSE19444 and GSE19435 gene chip data sets were downloaded from GEO database.GEO2R tool was used to screen differentially expressed genes between active PTB patients and healthy controls.CentiScape plug–in was used to determine the hub gene(Hub gene),and GSE56153 dataset was used to verify the changes of Hub gene during active PTB therapy.Results A total of 505 DEG,were selected from the two data sets,251 were up–regulated and 254 were down–regulated.The score of module 1 is the highest among the 9 sub–modules of DEG network,and 25 genes are mainly involved in virus defense,interferon signal pathway and other responses.Among the 25 genes,13 Hub genes were screened.The results showed that the expression levels of up–regulated IFIT3,IFI35,STAT1 and GBP1 decreased significantly in the first diagnosis of active PTB,8 weeks of treatment,28 weeks of treatment and the control group.Conclusion The screened IFIT3,IFI35,STAT1 and GBP1 are closely related to the occurrence and treatment of active PTB and are expected to be potential biomarkers for the diagnosis and treatment monitoring of PTB.