N6-methyladenosine promotes translation of VEGFA to accelerate angiogenesis in lung cancer.
Haisheng ZhangJiawang ZhouJiexin LiZhaotong WangZhuo-Jia ChenZiyan LvLichen GeGuoyou XieGuoming DengYalan RuiHong-Bing HuangLi-Kun ChenHong-Sheng WangPublished in: Cancer research (2023)
Angiogenesis is hijacked by cancer to support tumor growth. RNA modifications such as N6-methyladenosine (m6A) can regulate several aspects of cancer, including angiogenesis. Here, we find that m6A triggers angiogenesis in lung cancer by upregulating vascular endothelial growth factor-A (VEGFA), a central regulator of neovasculature and blood vessel growth. m6A-sequencing and functional studies confirmed that m6A modification of the 5'UTR of VEGFA positively regulates its translation. Specifically, methylation of a 5'UTR internal ribosome entry site (IRES) recruited the YTHDC2/eIF4GI complex to trigger cap-independent translation initiation. Intriguingly, the m6A methylation site A856 of the 5'UTR was located within the conserved upstream open reading frame (uORF) of VEGFA IRES-A, which overcomes uORF-mediated translation suppression while facilitating G-quadruplex-induced translation of VEGFA. Targeted specific demethylation of VEGFA m6A significantly decreased expression of VEGFA and reduced lung cancer cell-driven angiogenesis. In vivo and clinical data confirmed the positive effects of m6A modification of VEGFA on angiogenesis and tumor growth of lung cancer. This study not only reveals that the m6A/VEGFA axis is a potential target for lung cancer therapy but also expands our understanding of the impact of m6A modification of IRES in the 5'UTR of mRNA on translation regulation.
Keyphrases
- electronic health record
- vascular endothelial growth factor
- endothelial cells
- cancer therapy
- wound healing
- high glucose
- dna methylation
- drug delivery
- transcription factor
- papillary thyroid
- poor prognosis
- squamous cell carcinoma
- minimally invasive
- genome wide
- climate change
- artificial intelligence
- gene expression
- risk assessment
- lymph node metastasis
- young adults
- childhood cancer
- machine learning