As one of the three malignant genital tumors, mortality in women with ovarian cancer is consistently high worldwide. It is of great importance to find prognostic markers for diagnosis and treatment of ovarian cancer. In this study, the authors utilized the bioinformatics analysis to identify the potential key genes to reveal the potential mechanism for ovarian cancer. The authors used the gene expression profile (GSE14407) to perform differentially expressed gene (DEG) analysis and the weighted gene co-expression network analysis. They selected the key module and performed the gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for the genes in the hub module. Then they screened the key genes in the hub module, and further validated their expression level. A total of 3124 DEGs were detected after differential gene expression analysis; of these, 433 were upregulated genes and 2691 were downregulated genes. The authors selected the brown module that is significantly associated with the gene expression. Then they selected 30 hub genes from the protein-protein interaction network. And the authors identify the PDZ binding kinase () as the prognosis-associated hub gene whose expression was significantly high in the ovarian cancer tissue. The bioinformatics analysis for the DEGs could be important to understand the pathogenesis for ovarian cancer. In this study, is identified as a potential marker that might improve the understanding of the molecular mechanism and the diagnosis level for ovarian cancer.
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