Exploration of the sequential gene changes in epithelial ovarian cancer induced by carboplatin via microarray analysis

Wei,Shuqing; Liu,Jianwu; Shi,Yuxia; Zhang,Xi; Yang,Yongming; Song,Qingzhen

doi:10.3892/mmr.2017.7008

Exploration of the sequential gene changes in epithelial ovarian cancer induced by carboplatin via microarray analysis

Authors:
- Shuqing Wei
- Jianwu Liu
- Yuxia Shi
- Xi Zhang
- Yongming Yang
- Qingzhen Song
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Affiliations: Department of Geratology, Shanxi Tumor Hospital, Taiyuan, Shanxi 030013, P.R. China, Department of Urology, Shanxi Tumor Hospital, Taiyuan, Shanxi 030013, P.R. China, Department of Bone and Soft‑Tissue Tumor, Shanxi Tumor Hospital, Taiyuan, Shanxi 030013, P.R. China
Published online on: July 15, 2017 https://doi.org/10.3892/mmr.2017.7008
Pages: 3155-3160
Copyright: © Wei et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The purpose of the current study was to explore the carboplatin‑induced sequential changes in gene expression and screen out key genes, which were associated with effects of carboplatin on epithelial ovarian cancer (EOC). The microarray dataset GSE13525 was downloaded from the Gene Expression Omnibus database, including 6 EOC cell samples separately treated with carboplatin at 24, 30 and 36 h (case group), and 6 samples treated with phosphate‑buffered saline at the same time points (control group). A total of 3 sets of differentially expressed genes (DEGs) were respectively identified in case samples at 24, 30 and 36 h compared with the control group via the Limma package, and separately recorded as DEG‑24, DEG‑30 and DEG‑36. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the overlapped DEGs were performed via the Database for Annotation, Visualization and Integrated Discovery. The protein‑protein interaction (PPI) network was constructed and analyzed by Cytoscape software. In addition, the survival curves were drawn to illustrate the association between the expression levels of certain critical genes and the prognosis of EOC. A total of 170, 605 and 1043 DEGs were separately obtained in DEG‑24 DEG‑30 and DEG‑36, and 110 overlaps were identified. The overlaps were enriched in 77 GO terms and 3 KEGG pathways. A total of 152 pairs were involved in the PPI network, and the abnormal expression levels (high or low) of c‑Jun and cyclin B1 (CCNB1) would reduce the survival time of patients with EOC. The study indicated that c‑Jun and CCNB1 may be the prognostic biomarkers of EOC treated with carboplatin, and certain pathways (such as p53 signaling pathway, cell cycle and mitogen‑activation protein kinase signaling pathway) may be involved in carboplatin‑resistant EOC.

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