Deng Zhenxiang, Wang Wenhui, Li Jinming
Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.E-mail:
Nan Fang Yi Ke Da Xue Xue Bao. 2014 Jun;34(6):813-7.
To explore the pathogenesis of ovarian cancer from the perspective of molecular genetic variation and changes in mRNA expression profiles.
The data of DNA copy number and mRNA expression profiles of high-grade serious ovarian cancer were obtained from TCGA. The significant copy number variation regions were identified using the bioinformatics tool GISTIC, and the differentially expressed genes in these regions were identified using the samr package of SAM. The selected genes were subjected to bioinformatics analysis using GSEA tools.
GISTIC analysis identified 45 significant copy number amplification regions in ovarian cancer, and SAM and Fisher's exact test found that 40 of these genes showed altered expression levels. GSEA enrichment analysis revealed that most of these genes were reported in several published studies describing genetic study of tumorigenesis.
An integrative bioinformatics study of DNA copy number variation data and microarray data can identify genes involved in tumor pathogenesis. and offer new clues for studying early diagnosis and therapeutic target of ovarian cancer.
从分子遗传变异和mRNA表达谱变化的角度探讨卵巢癌的发病机制。
从TCGA获取高级别浆液性卵巢癌的DNA拷贝数和mRNA表达谱数据。使用生物信息学工具GISTIC识别显著的拷贝数变异区域，并使用SAM的samr包识别这些区域中差异表达的基因。使用GSEA工具对选定基因进行生物信息学分析。
GISTIC分析在卵巢癌中识别出45个显著的拷贝数扩增区域，SAM和Fisher精确检验发现其中40个基因的表达水平发生了改变。GSEA富集分析表明，这些基因中的大多数在几项描述肿瘤发生遗传研究的已发表研究中都有报道。
对DNA拷贝数变异数据和微阵列数据进行综合生物信息学研究，可以识别参与肿瘤发病机制的基因，并为研究卵巢癌的早期诊断和治疗靶点提供新线索。
