基于生物信息学途径探究卵巢癌关键预后基因

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摘要目的应用生物信息学途径探究卵巢癌关键预后基因,有助于了解卵巢癌发生发展的分子机制,为卵巢癌患者提供新的治疗靶点。方法从基因表达数据库(gene expression omnibus,GEO)中下载浆液性卵巢癌上皮细胞和正常卵巢上皮细胞芯片数据GSE14407、GSE18520、GSE54388和GSE66957,用R语言软件筛选差异基因并得到共同差异基因。应用clusterProfiler软件对共同差异基因进行GO功能分析及KEGG通路富集分析。使用STRING建立蛋白互作用网络并使用cytoscape选出关键基因。采用survival包、survimner包对关键基因进行预后生存分析。结果经过筛选得到305个共同差异基因,其中250个基因表达上调,55个基因表达下调。共同差异基因主要富集于染色体分离、细胞周期G1/S转变、细胞黏附、细胞间连接、磷脂酰肌醇-3-激酶/丝苏氨酸蛋白激酶(PI3K-AKT)信号通路、Rap1信号通路、细胞增殖调控和粘附斑激酶信号通路等。蛋白互作用网络筛选得到14个关键基因,生存分析显示UBE2C基因的高表达导致卵巢癌患者的总生存率明显降低。结论 UBE2C基因的表达与卵巢癌患者的总生存率密切相关,有望成为提高卵巢癌患者预后的新的生物学靶点。

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	许梦莹
	张广美

关键词 ：卵巢癌, 基因表达数据库, 差异表达基因, 关键基因

Abstract：Objective The application of bioinformatics approach to explore the key prognostic genes of ovarian cancer is expected to understand the molecular mechanism of the occurrence and development of ovarian cancer, and provide a new therapeutic target for patients with ovarian cancer.Methods Microarray data of tumor ovarian and normal ovarian epithelial cells GSE14407, GSE18520, GSE54388 and GSE66957 were downloaded from Gene Expression Omnibus (GEO) database. We used R language to screen the differentially expressed genes and obtain the common differentially expressed genes. ClusterProfiler software was used for GO function analysis and KEGG pathway enrichment analysis of differential genes. Protein interaction network was constructed with STRING and key genes were screened by cytoscape. Survival package and survimner package were used for survival analysis of key genes.Results Total 305 DEGs (differentially expressed genes), including 250 up-regulated genes and 55 down-regulated genes, were screened in this study. DEGs were mainly enriched in chromosome segregation, G1/S transformation of cell cycle, cell adhesion, intercellular connectivity, PI3K-AKT signaling pathway, Rap1 signaling pathway, cell proliferation regulation and focal adhesion kinase signaling pathway.14 key genes were screened by protein interaction network, and survival analysis showed that the high expression of UBE2C gene led to a significant reduction in the overall survival rate of ovarian cancer patients.Conclusion The expression of UBE2C gene is closely related to the survival rate of ovarian cancer patients and is expected to become a new biological target to enhance the prognosis of ovarian cancer patients.

Key words： Ovarian cancer GEO Differentially expressed genes Key genes

收稿日期: 2019-11-14

通讯作者: 张广美(guangmeizhang@126.com)

引用本文:

许梦莹, 张广美. 基于生物信息学途径探究卵巢癌关键预后基因[J]. 中国生育健康杂志, 2021, 32(1): 21-26. XU Mengying, ZHANG Guangmei. Exploring key prognostic genes of ovarian cancer based on bioinformatics approach. Chinese Journal of Reproductive Health, 2021, 32(1): 21-26.

链接本文:

http://cjrh.bjmu.edu.cn/CN/ 或 http://cjrh.bjmu.edu.cn/CN/Y2021/V32/I1/21

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