Integrated Analysis of the Gene Expression Profiling and Copy Number Aberration of the Ovarian Cancer

Abstract

Objective: DNA copy number alterations and difference expression are frequently observed in ovarian cancer. The purpose of this way was to pinpoint gene expression change that was associated with alterations in DNA copy number and could therefore enlighten some potential oncogenes and stability genes with functional roles in cancers, and investigated the bioinformatics significance for those correlated genes. Method: We obtained the DNA copy number and mRNA expression data from the Cancer Genomic Atlas and identified the most statistically significant copy number alteration regions using the GISTIC. Then identified the significance genes between the tumor samples within the copy number alteration regions and analyzed the correlation using a binary matrix. The selected genes were subjected to bioinformatics analysis using GSEA tool. Results: GISTIC analysis results showed there were 45 significance copy number amplification regions in the ovarian cancer, SAM and Fisher’s exact test found there have 40 genes can affect the expression level, which located in the amplification regions. That means we obtained 40 genes which have a correlation between copy number amplification and drastic up- and down-expression, which p-value ere overlapped with the several published studies which were focused on the gene study of tumorigenesis. Conclusion: The use of statistics and bioinformatics to analyze the microarray data can found an interaction network involved. The combination of the copy number data and expression has provided a short list of candidate genes that are consistent with tumor driving roles. These would offer new ideas for early diagnosis and treat target of ovarian cancer.