摘要Objective: To mine genes highly related to the pathogenesis of ovarian cancer by using multi-chip integrated bioinformatics methods and verify them in cells, which provided key genes and important theoretical basis for targeted research of ovarian cancer. Methods: Three datasets, GSE38666, GSE40595 and GSE54388, were downloaded from the Gene Expression Integrated Database database for differential gene (DEGs) screening, including 26 normal samples and 65 ovarian cancer samples. Gene ontology functional annotation of selected DEGs was performed through DAVID online database to clarify the biological characteristics of DEGs. The main pathways of DEGs were obtained by enrichment analysis using Kyoto gene and genomic encyclopedia method. Based on the STRING database, the DEGs protein-protein interaction network was constructed by using CytoScape software, and the key genes were screened by GEPIA2 database to verify the expression at the cell level. Results: A total of 238 DEGs were screened from GSE38666, GSE40595 and GSE54388 datasets, of which 168 DEGs were up-regulated and 70 DEGs were down-regulated. The co-expressed DEGs were mainly enriched in biological functions such as mitotic nuclear division, spindle, chromosomal region and DNA helicase activity in ovarian cancer. They were mainly involved in biological processes such as cell cycle, DNA replication, oxidative phosphorylation and biosynthesis of amino acids, thereby affecting the occurrence and development of ovarian cancer. Six genes were highly expressed and associated with the development of ovarian cancer, including IFI27, EPCAM, CXCR4, PEA15, CLDN3 and CAPG. Cell verification showed that the mRNA expression of the six genes in ovarian cancer cells was higher than that in normal ovarian cells (P<0.05), which was consistent with the previous screening results. Conclusion: Multi-chip integrated bioinformatics is an effective method to find ovarian cancer target genes. IFI27, EPCAM, CXCR4, PEA15, CLDN3 and CAPG are highly correlated with the occurrence and development of ovarian cancer, which can be used as target genes for ovarian cancer research.