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KMID : 0620920180500100136
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Experimental & Molecular Medicine
2018 Volume.50 No. 10 p.136 ~ p.136
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Cyclin B1 stability is increased by interaction with BRCA1, and its overexpression suppresses the progression of BRCA1-associated mammary tumors
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Choi Eun-Kyung
Lim Jeong-A
Kim Jong-Kwang
Jang Moon-Sun
Kim Sun-Eui
Baek Hye-Jung
Park Eun-Jung
Kim Tae-Hyun
Deng Chu-Xia
Wang Rui-Hong
Kim Sang-Soo
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Abstract
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Germline BRCA1 mutations predispose women to breast and ovarian cancer. BRCA1, a large protein with multiple functional domains, interacts with numerous proteins involved in many important biological processes and pathways. However, to date, the role of BRCA1 interactions at specific stages in the progression of mammary tumors, particularly in relation to cell cycle regulation, remains elusive. Here, we demonstrate that BRCA1 interacts with cyclin B1, a crucial cell cycle regulator, and that their interaction is modulated by DNA damage and cell cycle phase. In DNA-damaged mitotic cells, BRCA1 inhibits cytoplasmic transportation of cyclin B1, which prevents cyclin B1 degradation. Moreover, restoration of cyclin B1 in BRCA1-deficient cells reduced cell survival in association with induction of apoptosis. We further demonstrate that treatment of Brca1-mutant mammary tumors with vinblastine, which induces cyclin B1, significantly reduced tumor progression. In addition, a correlation analysis of vinblastine responses and gene expression profiles in tumors at baseline revealed 113 genes that were differentially expressed between tumors that did and did not respond to vinblastine treatment. Further analyses of protein?protein interaction networks revealed gene clusters related to vinblastine resistance, including nucleotide excision repair, epigenetic regulation, and the messenger RNA surveillance pathway. These findings enhance our understanding of how loss of BRCA1 disrupts mitosis regulation through dysregulation of cyclin B1 and provide evidence suggesting that targeting cyclin B1 may be useful in BRCA1-associated breast cancer therapy.
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KEYWORD
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Breast cancer, Cancer models, Predictive markers
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