基于数据挖掘分析KIF14与SSK1在卵巢癌组织中的表达及临床意义  

Analysis of the expression and clinical significance of KIF14 and SSK1 in ovarian cancer based on data mining

作　　者：蒋郁竹 娄阁[1] JIANG Yuzhu;LOU Ge(Harbin Medical University Cancer Hospital,Heilongjiang Harbin 150081,China.)

机构地区：[1]哈尔滨医科大学附属肿瘤医院,黑龙江哈尔滨150081

出　　处：《现代肿瘤医学》2021年第16期2880-2886,共7页Journal of Modern Oncology

基　　金：国家自然科学基金(编号:81872507)。

摘　　要：目的:应用生物信息学方法挖掘卵巢癌的相关基因,探讨其表达情况及临床意义,为卵巢癌的诊断和靶向治疗提供依据。方法:从GEO(Gene Expression Omnibus)数据库下载基因芯片数据集GSE23391、GSE18520和GSE54388,利用其在线分析工具GEO2R筛选卵巢癌的差异表达基因(DEGs)。运用数据库DAVID进行GO功能和KEGG通路富集分析,应用Cytoscape软件和STRING数据库构建蛋白互作网络和关键基因模块,挖掘卵巢癌靶基因。利用Oncomine在线分析网站,分析癌症公共数据基因组中卵巢癌KIF14及SSK1 mRNA的表达信息,利用GEPIA、Kaplan-Meier Plotter进行患者生存分析。结果:共筛选出251个DEGs,富集分析显示差异基因在减数分裂、丝氨酸/苏氨酸代谢、启动DNA复制、细胞衰老等方面富集。筛选获得22个DEGs均呈高表达,其中KIF14与SSK1的表达水平与卵巢癌的FIGO分期及总生存率明显相关。结论:本研究通过生物信息学挖掘,发现KIF14与SSK1基因在卵巢癌组织中呈高表达,并且与卵巢癌的预后关系密切,具有指导意义。Objective:To explore the related genes of ovarian cancer by applying bioinformatical,and provide a basis for the diagnosis and targeted therapy of ovarian cancer.Methods:Download gene chip data sets GSE23391,GSE18520 and GSE54388 from GEO(Gene Expression Omnibus)database,and use its online analysis tool GEO2R to screen differentially expressed genes(DEGs)in ovarian cancer.Use the database DAVID for GO function and KEGG pathway enrichment analysis,use Cytoscape software and STRING database to construct protein interaction network and key gene modules,and mine ovarian cancer target genes.Use Oncomine online analysis website to analyze the expression information of ovarian cancer KIF14 and SSK1 mRNA in the cancer public data genome,and use GEPIA and Kaplan-Meier Plotter for patient survival analysis.Results:A total of 251 DEGs were screened out.The enrichment analysis showed that the differential genes were enriched in meiosis,serine/threonine metabolism,initiation of DNA replication,and cell senescence.22 DEGs were screened and all showed high expression.Among them,the expression levels of KIF14 and SSK1 were significantly related to the FIGO staging and overall survival rate of ovarian cancer.Conclusion:Through bioinformatics mining,this study found that KIF14 and SSK1 genes are highly expressed in ovarian cancer tissues,and are closely related to the prognosis of ovarian cancer,which has guiding significance.

关 键 词：卵巢癌 差异表达基因 生物信息学 KIF14 SSK1 

分 类 号：R737.31[医药卫生—肿瘤]