Aberrant promoter hypermethylation inhibits RGMA expression and contributes to tumor progression in breast cancer.
Yan LiHai-Ting LiuXu ChenYa-Wen WangYa-Ru TianRan-Ran MaLin SongYong-Xin ZouPeng GaoPublished in: Oncogene (2021)
Breast cancer (BC) is the most common cancer in women worldwide, and the exploration of aberrantly expressed genes might clarify tumorigenesis and help uncover new therapeutic strategies for BC. Although RGMA was recently recognized as a tumor suppressor gene, its detailed biological function and regulation in BC remain unclear. Herein, we found that RGMA was downregulated in BC tissues compared with non-tumorous breast tissues, particularly in metastatic BC samples, and that patients with low RGMA expression manifested a poorer prognosis. Furthermore, DNMT1 and DNMT3A were found to be recruited to the RGMA promoter and induced aberrant hypermethylation, resulting in downregulation of RGMA expression in BC. In contrast, RGMA overexpression suppressed BC cell proliferation and colony-formation capabilities and increased BC cell apoptosis. Furthermore, RGMA knockdown accelerated BC cell proliferation and suppressed cellular apoptosis in vitro and in vivo. Reversal of RGMA promoter methylation with 5-Aza-CdR restored RGMA expression and blocked tumor growth. Overall, DNMT1- and DNMT3A-mediated RGMA promoter hypermethylation led to downregulation of RGMA expression, and low RGMA expression contributed to BC growth via activation of the FAK/Src/PI3K/AKT-signaling pathway. Our data thus suggested that RGMA might be a promising therapeutic target in BC.
Keyphrases
- cell proliferation
- poor prognosis
- dna methylation
- pi k akt
- signaling pathway
- gene expression
- genome wide
- small cell lung cancer
- long non coding rna
- cell cycle arrest
- cell cycle
- binding protein
- magnetic resonance imaging
- oxidative stress
- epithelial mesenchymal transition
- metabolic syndrome
- magnetic resonance
- skeletal muscle
- endoplasmic reticulum stress
- artificial intelligence
- drug induced
- contrast enhanced
- machine learning
- childhood cancer