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

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.