Literature DB >> 34366326

E2F1 transcriptionally regulates CCNA2 expression to promote triple negative breast cancer tumorigenicity.

Yongbin Lu1,2,3,1, Fei Su4,1, Hui Yang5,1, Yi Xiao6, Xiaobin Zhang6, Hongxin Su7, Tao Zhang4, Yana Bai2,8, Xiaoling Ling4.   

Abstract

BACKGROUND: Triple-negative breast cancer (TNBC) is a highly malignant breast cancer subtype with a poor prognosis. The cell cycle regulator cyclin A2 (CCNA2) plays a role in tumor development. Herein, we explored the role of CCNA2 in TNBC.
METHODS: We analyzed CCNA2 expression in 15 pairs of TNBC and adjacent tissues and assessed the relationship between CCNA2 expression using the tissue microarray cohort. Furthermore, we used two TNBC cohort datasets to analyze the correlation between CCNA2 and E2F transcription factor 1 (E2F1) and a luciferase reporter to explore their association. Through rescue experiments, we analyzed the effects of E2F1 knockdown on CCNA2 expression and cellular behavior.
RESULTS: We found that CCNA2 expression in TNBC was significantly higher than that in adjacent tissues with similar observations in MDA-MB-231 and MDA-MB-468 cells. E2F1 was highly correlated with CCNA2 as observed through bioinformatics analysis (R= 0.80, P< 0.001) and through TNBC tissue verification analysis (R= 0.53, P< 0.001). We determined that E2F1 binds the +677 position within the CCNA2 promoter. Moreover, CCNA2 overexpression increased cell proliferation, invasion, and migration owing to E2F1 upregulation in TNBC.
CONCLUSION: Our data indicate that E2F1 promotes TNBC proliferation and invasion by upregulating CCNA2 expression. E2F1 and CCNA2 are potential candidates that may be targeted for effective TNBC treatment.

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Keywords:  CCNA2; E2F1; TNBC; tumorigenicity

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Year:  2022        PMID: 34366326     DOI: 10.3233/CBM-210149

Source DB:  PubMed          Journal:  Cancer Biomark        ISSN: 1574-0153            Impact factor:   4.388


  1 in total

1.  Identification of Key Prognostic Genes of Triple Negative Breast Cancer by LASSO-Based Machine Learning and Bioinformatics Analysis.

Authors:  De-Lun Chen; Jia-Hua Cai; Charles C N Wang
Journal:  Genes (Basel)       Date:  2022-05-18       Impact factor: 4.141

  1 in total

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