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山东大学学报 (医学版) ›› 2022, Vol. 60 ›› Issue (8): 63-71.doi: 10.6040/j.issn.1671-7554.0.2022.1479

• 临床医学 • 上一篇    下一篇

基于生物信息库病例分析ECT2在子宫内膜癌中的表达及临床意义

刘腾,马迎春   

  1. 山东第一医科大学第一附属医院(山东省千佛山医院)妇科, 山东 济南 250012
  • 发布日期:2022-07-27
  • 通讯作者: 马迎春. E-mail:yingchunma74@163.com

Expression of ECT2 in uterine corpus endometrial carcinoma and its clinical significance based on bioinformatics database

LIU Teng, MA Yingchun   

  1. The First Affiliated Hospital of Shandong First Medical University &
    Shandong Provincial Qianfoshan Hospital, Jinan 250012, Shandong, China
  • Published:2022-07-27

摘要: 目的 通过多种数据库分析ECT2在子宫内膜癌(UCEC)的表达水平、预后情况及潜在功能。 方法 利用GEO、STRING、Cytoscape筛选UCEC关键基因。联合TIMER2.0、UALCAN、肿瘤基因组图谱(TCGA)进行预后分析,运用LinkedOmics及Metascape在线分析工具行共表达基因筛选和功能富集分析。TIMER2.0分析关键基因与多肿瘤免疫浸润细胞关联性。 结果 共筛选出245个DEGs,ECT2为关键基因,其表达水平与UCEC肿瘤分级、分期及预后生存期有关联,另外发现ECT2与肿瘤增殖和免疫浸润细胞有关联。 结论 ECT2在UCEC中高表达,且与UCEC的不良预后有一定的关联性,这可能为UCEC治疗提供新靶点。

关键词: 子宫内膜癌, GEO数据库, 生物信息学分析, 免疫细胞, 细胞周期

Abstract: Objective To analyze the expression and potential function of ECT2 in uterine corpus endometrial carcinoma(UCEC)through multiple databases and to explore the prognosis. Methods The hub genes of UCEC were screened with GEO, STRING and Cytoscape. Kaplan-Meier analysis was performed in combination with TIMER2.0, UALCAN and Tumor Genome Atlas(TCGA). The differentially expressed genes(DEGs)were screened and functional enrichment analysis was performed using online analysis tools of Linked Omics and Metascape. The correlation between hub genes and pan-cancer immune infiltrating cells was analyzed with TIMER2.0. Results A total of 245 DEGs were screened out. ECT2 was the key gene, and its expression was correlated with the grade, stage, prognosis and survival of UCEC patients. ECT2 was also correlated with tumor proliferation and immune infiltration. Conclusion ECT2 is highly expressed in UCEC and closely related to the dismal prognosis, which may provide a new target for the treatment of this disease.

Key words: Uterine corpus endometrial carcinoma, GEO datebase, Bioinformatics analysis, Immune cells, Cell cycle

中图分类号: 

  • R737.33
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