中国癌症杂志 ›› 2021, Vol. 31 ›› Issue (9): 807-816.doi: 10.19401/j.cnki.1007-3639.2021.09.006

• 论著 • 上一篇    下一篇

Hsa-miR-98-5p/DKK3信号轴对乳腺癌细胞生物学行为的影响

姚 嘉 1 ,李冠乔 1 ,杨时平 2 ,苏慧銮 3   

  1. 1. 海南省人民医院乳腺外科,海南 海口 570311 ;
    2. 海南省人民医院放疗科,海南 海口 570311 ;
    3. 海南省人民医院肾内科,海南 海口 570311
  • 出版日期:2021-09-30 发布日期:2021-10-08
  • 通信作者: 姚 嘉 E-mail: javance@163.com
  • 基金资助:
    海南省自然科学基金青年项目(818QN314)。

Effect of hsa-miR-98-5p/DKK3 signal axis on biological behavior of breast cancer cells

YAO Jia 1 , LI Guanqiao 1 , YANG Shiping 2 , SU Huiluan   

  1. 1. Department of General Surgery-Breast Center, Hainan General Hospital, Haikou 570311, Hainan Province, China; 2. Department of Radiotherapy, Hainan General Hospital, Haikou 570311, Hainan Province, China; 3. Department of Nephrology, Hainan General Hospital, Haikou 570311, Hainan Province, China
  • Published:2021-09-30 Online:2021-10-08
  • Contact: YAO Jia E-mail: javance@163.com

摘要: 背景与目的:乳腺癌威胁着全世界女性的健康。虽然已发现大量微小RNA(microRNA,miRNA)在乳腺癌中异常表达,但仍需要构建一个完整的miRNA-信使RNA(messenger RNA,mRNA)网络。方法:利用癌症基因组图谱(The Cancer Genome Atlas,TCGA)数据库下载乳腺癌相关数据集,分析肿瘤组织与正常组织之间差异表达的miRNA。利用miRDB、miRTarBase和StarBase数据库分析差异miRNA靶向的基因。使用R语言中的ClusterProfiler包对靶基因进行富集分析。使用String数据库联合Cytoscape 3.6.2软件进行蛋白质-蛋白质相互作用(protein-protein interaction,PPI)网络分析及Hub基因的筛选;构建miRNA-Hub mRNA调控网络确定研究信号轴,然后通过细胞实验进行验证。结果:采用TCGA数据集识别出两个差异miRNAs。3个数据库取交集预测得到278个靶基因。共鉴定出10个Hub基因,从构建的miRNA-Hub基因网络图中发现,hsa-miR-98-5p/DKK3轴可能在乳腺癌的进展中起关键作用。细胞功能学实验证实hsa-miR-98-5p可抑制细胞凋亡,促进细胞的增殖、迁移和侵袭。双荧光素酶报告基因实验进一步验证了hsa-miR-98-5p与DKK3的结合作用。结论:本研究首先通过生物信息学分析鉴定出一个与乳腺癌进展相关的hsa-miR-98-5p/DKK3轴,初步证实hsa-miR-98-5p可通过靶向DKK3抑制乳腺癌细胞凋亡,促进细胞的增殖、迁移和侵袭。

关键词: 乳腺癌, Hsa-miR-98-5p, DKK3, 双荧光素酶活性报告实验

Abstract: Background and purpose: Breast cancer threatens the health of women all over the world. Although a large number of microRNAs (miRNAs) have been found to be abnormally expressed in breast cancer, a complete miRNA-messenger RNA (mRNA) network still needs to be constructed. Methods: The Cancer Genome Atlas (TCGA) database was used to download breast cancer related data sets and analyze the differentially expressed miRNAs between tumor tissues and normal tissues. The miRDB, miRTarBase and StarBase databases were used to analyze the genes targeted by different miRNAs. The ClusterProfiler package in R language was used to enrich and analyze the target genes. String database and Cytoscape 3.6.2 software were used to analyze protein- protein interaction (PPI) network and screen Hub gene. The miRNA-Hub mRNA regulatory network was constructed to determine the research signal axis, and then verified by cell experiments. Results: Two differential miRNAs were identified in TCGA data set; 278 target genes were predicted from the three databases. Ten Hub genes were identified. The constructed miRNA Hub gene network showed that hsa-mir-98-5p/DKK3 axis might play a key role in the progression of breast cancer. Cell functional experiments confirmed that hsa-miR-98-5p could inhibit apoptosis and promote cell proliferation, migration and invasion. The binding of hsa- miR-98-5p to DKK3 was further confirmed by dual luciferase activity assay. Conclusion: In this study, we analyzed a miRNA- mRNA network associated with breast cancer progression and identified an important miRNA mRNA axis in breast cancer.

Key words: Breast cancer, Hsa-miR-98-5p, DKK3, Dual luciferase activity report experiment