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

An integrative bioinformatics study of DNA copy number variation and differentially expressed genes in ovarian cancer

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。

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＆#39;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.