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.