[1]邓祯祥,王文辉,李金明.整合卵巢癌染色体拷贝数变异与差异表达基因的生物信息学分析[J].南方医科大学学报,2014,(06):813.
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整合卵巢癌染色体拷贝数变异与差异表达基因的生物信息学分析()
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《南方医科大学学报》[ISSN:/CN:]

卷:
期数:
2014年06期
页码:
813
栏目:
出版日期:
2014-06-15

文章信息/Info

Title:
An integrative bioinformatics study of DNA copy number variation and differentially
expressed genes in ovarian cancer
作者:
邓祯祥王文辉李金明
关键词:
卵巢癌拷贝数变异基因差异表达SAM方法GISTIC方法
Keywords:
ovarian cancer copy number variation differentially expressed genes SAM method GISTIC method
摘要:
目的从分子的遗传变异和表达水平综合探讨卵巢癌的发病机制,为临床诊疗提供新思路。方法从TCGA数据门户上下
载大样本的高浆液性卵巢癌DNA拷贝数数据和mRNA表达数据,使用GISTIC对拷贝数变异进行分析,利用SAM软件包samr
筛选差异表达基因;并利用GSEA等工具进行生物信息学分析。结果GISTIC 发现45 个拷贝数扩增区域;SAM和Fisher’s
exact test发现拷贝数扩增区域中有40个拷贝数变异的基因能引起表达差异;GSEA富集分析发现这些拷贝数变异基因主要富
集在多个有关癌症基因集的研究报告中。结论利用生物信息学方法综合分析拷贝数变异数据和基因表达数据,能充分有效地
获取信息,为确定卵巢癌的早期诊断和治疗靶点提供新的思路。
Abstract:
Objective To explore the pathogenesis of ovarian cancer from the perspective of molecular genetic variation and
changes in mRNA expression profiles. Method 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. Results 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. Conclusion 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.

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更新日期/Last Update: 1900-01-01