| 摘要: |
| 目的 寻找卵巢癌预后的关键基因，为卵巢癌治疗提供新的靶点。方法 从基因表达汇编（GEO）数据库GSE18520和GSE14407数据集、癌症基因组图谱（TCGA）数据库及基因型-组织表达（GTEx）数据库中下载卵巢癌相关数据，用R 3.6.2软件limma包进行差异表达基因分析，随后使用R 3.6.2软件clusterProfiler包对差异表达基因进行基因本体（GO）及京都基因与基因组百科全书（KEGG）富集分析。使用STRING数据库建立蛋白质-蛋白质相互作用网络，利用Cytoscape软件cytoHubba插件筛选核心基因，利用基因表达谱交互分析（GEPIA）数据库验证核心基因在卵巢癌组织中的表达情况，随后使用Kaplan-Meier Plotter数据库对核心基因进行生存分析。结果 通过GEO数据库GSE18520、GSE14407数据集及TCGA、GTEx数据库共同筛选获得69个差异表达基因，主要富集在ABC转运体、视黄醇代谢及Wnt信号通路。蛋白质-蛋白质相互作用网络分析提示共有9个核心基因，GEPIA数据库分析结果表明这9个基因在卵巢癌中高表达。Kaplan-Meier Plotter数据库分析结果表明，中心体相关蛋白55（CEP55）、序列相似性83家族蛋白成员D（FAM83D）、驱动蛋白家族成员20A（KIF20A）、细胞周期依赖性激酶亚基蛋白2（CKS2）和中心体相关激酶2（NEK2）基因高表达的卵巢癌患者总生存期比低表达的患者缩短，CEP55、FAM83D、KIF20A、叉头框蛋白M1（FOXM1）和TTK蛋白激酶（TTK）基因高表达的患者无进展生存期比低表达的患者缩短。结论 CEP55、FAM83D、KIF20A、CKS2、NEK2、FOXM1和TTK的表达与卵巢癌患者的预后密切相关。 |
| 关键词: 卵巢肿瘤 预后 生物信息学 差异表达基因 |
| DOI：10.16781/j.CN31-2187/R.20211296 |
| 投稿时间：2021-12-23修订日期：2022-01-27 |
| 基金项目:海军军医大学（第二军医大学）第一附属医院“234学科攀峰计划”（2019YXK014），深蓝123重点攻关项目（2020YSL009），海军计生课题（19JSZ05）. |
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| Prognostic genes in ovarian cancer: a multi-database analysis |
| WANG Jing1△,SU Xiao-ling2△,HE Hai-wei1,WANG Zhi-ming1,LU Nan1,XU Ming-juan1* |
(1. Department of Obstetrics and Gynecology, The First Affiliated Hospital of Naval Medical University(Second Military Medical University), Shanghai 200433, China;
2. Department of Obstetrics and Gynecology, Naval Medical Center, Naval Medical University(Second Military Medical University), Shanghai 200433, China
△Co-first authors.
* Corresponding author) |
| Abstract: |
| Objective To search for the hub genes for the prognosis of ovarian cancer and provide new targets for the treatment of ovarian cancer. Methods Ovarian cancer related data were downloaded from Gene Expression Omnibus (GEO) database (GSE18520 and GSE14407 datasets), The Cancer Genome Atlas (TCGA) database and the Genotype-Tissue Expression (GTEx) database. Differentially expressed genes were analyzed with limma package of R 3.6.2 software, and then clusterProfiler package was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of these genes. Meanwhile, STRING was used to establish the protein-protein interaction network, and cytoHubba package of Cytoscape software was used to screen the hub genes. Gene Expression Profile Interaction Analysis (GEPIA) database was used to verify the expression of hub genes in ovarian cancer tissues. Then, Kaplan-Meier Plotter database was used to perform survival analysis on the hub genes. Results A total of 69 differentially expressed genes were screened by GEO (GSE18520 and GSE14407), TCGA and GTEx databases, and they were mainly enriched in the ABC transporter, retinol metabolism and Wnt signaling pathways. Protein-protein interaction network analysis showed that there were 9 hub genes, which were verified in GEPIA. Kaplan-Meier Plotter database analysis showed that the overall survival was shorter in the ovarian cancer patients with high expression of centrosomal protein 55 (CEP55), family with sequence similarity 83, member D (FAM83D), kinesin family member 20A (KIF20A), cyclin dependent-kinase subunit protein 2 (CKS2) and NIMA related kinase 2 (NEK2) genes; and the progression-free survival was shorter in patients with high expression of CEP55, FAM83D, KIF20A, forkhead box protein M1 (FOXM1) and TTK protein kinase (TTK) than those with low expression. Conclusion The expression of CEP55, FAM83D, KIF20A, CKS2, NEK2, FOXM1 and TTK are closely related to the prognosis of ovarian cancer patients. |
| Key words: ovarian neoplasms prognosis bioinformatics differentially expressed genes |
