Identification of potential targets for ovarian cancer treatment by systematic bioinformatics analysis

Purpose of investigation: To provide a systematic overview to understand the mechanism of ovarian cancer. Materials and Methods: Data of GSE14407 downloaded from Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were identified. Gene ontology and pathway enrichment analysis were performed by Database for Annotation, Visualization and Integrated Discovery (DAVID). Furthermore, the authors constructed the protein-protein interaction (PPI) network and co-expression networks by Cytoscape. Results: A total 1,442 genes were identified to be differentially expressed. Regulatory effects of DEGs mainly focused on cell cycle, transcription regulation, and cellular protein metabolic process. Significant pathways were determined to be p53 signaling pathway, amino sugar, and nucleotide sugar metabolism. The most significant transcription factor was aryl hydrocarbon receptor nuclear translocator (ARNT). Abnormal spindle-like microcephaly-associated protein (ASPM), Aurora kinase (AURKA), Cyclin-A2 (CCNA2), G2/mitotic-specific cyclin-B1, (CCNB1), and Cyclin-dependent kinase 1 (CDK1) were significant nodes in PPI network. Conclusion: The significant genes and pathways show potential targets for the treatment of ovarian cancer.