Bioinformatics analysis to screen the key prognostic genes in ovarian cancer.
Bioinformatics analysis to screen the key prognostic genes in ovarian cancer.
复制标题
生物信息学分析筛选卵巢癌关键预后基因
DOI：
10.1186/s13048-017-0323-6
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发表时间：
2017-04-13
影响因子：
4
通讯作者：
Zhang J
中科院分区：
文献类型：
--
作者：
Li L;Cai S;Liu S;Feng H;Zhang J
卵巢癌是一种预后差、病死率高的妇科肿瘤。本研究旨在通过生物信息学分析寻找影响OC预后的关键基因。方法从癌症基因组图谱数据库下载OC患者的基因表达数据(包括568例原发OC组织、17例复发OC组织和8例癌旁正常组织)及相关临床信息。数据预处理后，利用R中的ConensusClusterPlus软件包进行聚类分析，利用R中的LIMMA软件包进行差异分析以确定特征基因。在Kaplan-Meier(Km)生存分析的基础上，从特征基因中筛选出预测预后的种子基因。通过聚类分析和KM生存分析进一步筛选出关键预后基因后，进行功能浓缩分析和多因素生存分析。结果共获得3668个特征基因，其中75个基因被鉴定为预后种子基因。筛选出AXL、FOS、KLF6、WDR77、DUSP1、GADD45B、SLIT3等25个关键预后基因。尤其是AXLandSLIT3在排卵周期中表现出较高的丰度。多因素生存分析显示，关键预后基因能有效区分样本，与预后显著相关。结论AXL、FOS、KLF6、WDR77、DUSP1、GADD45B、SLIT3可能影响卵巢癌的预后。
BackgroundOvarian cancer (OC) is a gynecological oncology that has a poor prognosis and high mortality. This study is conducted to identify the key genes implicated in the prognosis of OC by bioinformatic analysis.MethodsGene expression data (including 568 primary OC tissues, 17 recurrent OC tissues, and 8 adjacent normal tissues) and the relevant clinical information of OC patients were downloaded from The Cancer Genome Atlas database. After data preprocessing, cluster analysis was conducted using the ConsensusClusterPlus package in R. Using the limma package in R, differential analysis was performed to identify feature genes. Based on Kaplan-Meier (KM) survival analysis, prognostic seed genes were selected from the feature genes. After key prognostic genes were further screened by cluster analysis and KM survival analysis, they were performed functional enrichment analysis and multivariate survival analysis. Using the survival package in R, cox regression analysis was conducted for the microarray data of GSE17260 to validate the key prognostic genes.ResultsA total of 3668 feature genes were obtained, among which 75 genes were identified as prognostic seed genes. Then, 25 key prognostic genes were screened, includingAXL,FOS,KLF6,WDR77,DUSP1,GADD45B, andSLIT3. Especially,AXLandSLIT3were enriched in ovulation cycle. Multivariate survival analysis showed that the key prognostic genes could effectively differentiate the samples and were significantly associated with prognosis. Additionally, GSE17260 confirmed that the key prognostic genes were associated with the prognosis of OC.ConclusionAXL,FOS,KLF6,WDR77,DUSP1,GADD45B, andSLIT3might affect the prognosis of OC.