An integrative bioinformatics study of DNA copy number variation and differentially expressed genes in ovarian cancer
10.3969/j.issn.1673-4254.2014.06.12
- VernacularTitle: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
- Author:
Zhenxiang DENG
1
;
Wenhui WANG
;
Jinming LI
Author Information
1. 南方医科大学基础医学院生物信息学系
- Keywords:
ovarian cancer;
copy number variation;
differentially expressed genes;
SAM method;
GISTIC method
- From:
Journal of Southern Medical University
2014;(6):813-817
- CountryChina
- Language:Chinese
-
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.
