生物芯片高通量检测卵巢癌转录组差异表达基因的生物标记识别

Microarray data analysis and biomarker identification of ovarian cancer

目的探究卵巢癌中基因的表达情况并识别重要的代谢通路。方法从Array Express数据库下载卵巢癌芯片数据,使用rank product方法识别差异表达基因;利用STRING900、STRING、Bossi&Lehner和PSICQUIC四个数据库对这些差异基因进行KEGG通路富集分析,并运用R语言运算取这些通路的交集,以找出在卵巢癌发生中重要的代谢通路;最后构建各通路中重叠基因的网络节点图,以识别通路中的重要基因。结果在卵巢癌中共检测到1,732个差异表达基因和176条代谢通路。其中,p53信号通路、酪氨酸代谢、鞘脂类代谢是卵巢癌中最重要的代谢通路。这些通路中基因的表达均发生了显著变化。结论所识别的重要代谢通路和这些通路中基因表达的变化可作为卵巢癌诊断的潜在生物标记。

Objective To explore the gene expression pattern and identify the important metabolicpathways in ovarian cancer.Methods Themicroarray data of ovarian cancer was downloaded from ArrayExpressdatabase,and the differentially expressed(DE)genes in it were identified by the rank product method.TheKEGG pathway enrichment analysis of these DE genes was performed by using the on line database STRING900,STRING,Bossi&Lehner and PSICQUIC.The R language was used to computing the intersection of these pathwaysand finding out the important pathways in ovarian cancer.Finally,the important genes and their relationsin these pathways were analyzed by constructing the nodes network of the overlapped genes.Results A total of1,732DE genes and176metabolic pathways were involved in the occurrence.The p53signaling pathway,tyrosinemetabolism and sphingolipid metabolism were the most important pathways in ovarian cancer.The expressionpattern of the genes that involved in these pathways was also greatly changed.Conclusion The importantpathways and change of the genes involved in them can be regard as the underlying biomarkers for the diagnosisof ovarian cancer.