Role of NSC 319726 in ovarian cancer based on the bioinformatics analyses

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Abstract

Aim: This study aimed to explore the molecular mechanisms of NSC319726 in ovarian cancer by bioinformatics analyses. Methods: Gene expression profile GSE35972 was downloaded from the Gene Expression Omnibus database. The data set contains six samples, including three samples of TOV112D cells untreated and three samples of TOV112D cells treated with NSC319726. The differentially expressed genes (DEGs) between untreated and treated samples were analyzed using the limma package. Kyoto Encyclopedia of Genes and Genomes pathway analysis was performed using the signaling pathway impact analysis package, followed by the functional annotation of DEGs and protein–protein interaction network construction. Finally, the subnetwork was identified, and Gene Ontology functional enrichment analysis was performed on the DEGs enriched in the subnetwork. Results: A total of 120 upregulated and 126 downregulated DEGs were identified. Six Kyoto Encyclopedia of Genes and Genomes pathways were significantly perturbed by DEGs, and the pathway of oocyte meiosis was identified as the most perturbed one. Oocyte meiosis was enriched by eight downregulated DEGs, such as ribosomal protein S6 kinase, 90 kDa, and polypeptide 6 (RPS6KA6). After functional annotation, eight transcription factors were upregulated (such as B-cell CLL/lymphoma 6 [BCL6]), and three transcription factors were downregulated. Seven tumor suppressor genes, such as forkhead box O3 (FOXO3), were upregulated. Additionally, in the protein–protein interaction network and subnetwork, cyclin B1 (CCNB1) and cell division cycle 20 (CDC20) were hub genes, which were also involved in the functions related to mitotic cell cycle. Conclusion: NSC319726 may play an efficient role against ovarian cancer via targeting genes, such as RPS6KA6, BCL6, FOXO3, CCNB1, and CDC20, which are involved in oocyte meiosis pathway.

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Xue, J., Yang, G., Ding, H., Wang, P., & Wang, C. (2015). Role of NSC 319726 in ovarian cancer based on the bioinformatics analyses. OncoTargets and Therapy, 8, 3757–3765. https://doi.org/10.2147/OTT.S86343

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