• Title/Summary/Keyword: MIRN497

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miR-195/miR-497 Regulate CD274 Expression of Immune Regulatory Ligands in Triple-Negative Breast Cancer

  • Yang, Lianzhou;Cai, Yuchen;Zhang, Dongsheng;Sun, Jian;Xu, Chenyu;Zhao, Wenli;Jiang, Wenqi;Pan, Chunhua
    • Journal of Breast Cancer
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    • v.21 no.4
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    • pp.371-381
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    • 2018
  • Purpose: Immune suppression is common in patients with advanced breast cancer but the mechanisms underlying this phenomenon have not been sufficiently studied. In this study, we aimed to identify B7 family members that were able to predict the immune status of patients, and which may serve as potential targets for the treatment of breast cancer. We also aimed to identify microRNAs that may regulate the expression of B7 family members. Methods: The Cancer Genome Atlas data from 1,092 patients with breast cancer, including gene expression, microRNA expression and survival data, were used for statistical and survival analyses. Polymerase chain reaction and Western blot were used to measure messenger RNA and protein expression, respectively. Luciferase assay was used to investigate direct microRNA target. Results: Bioinformatic analysis predicted that microRNA (miR)-93, miR-195, miR-497, and miR-340 are potential regulators of the immune evasion of breast cancer cells, and that they exert this function by targeting CD274, PDCD1LG2, and NCR3LG1. We chose CD274 for further investigations. We found that miR-195, miR-497, and CD274 expression levels were inversely correlated in MDA-MB-231 cells, and miR-195 and miR-497 expressions mimic inhibited CD274 expression in vitro. Mechanistic investigations demonstrated that miR-195 and miR-497 directly target CD274 3' untranslated region. Conclusion: Our data indicated that the level of B7 family members can predict the prognosis of breast cancer patients, and miR-195/miR-497 regulate CD274 expression in triple negative breast cancer. This regulation may further influence tumor progression and the immune tolerance mechanism in breast cancer and may be able to predict the effect of immunotherapy on patients.