ESCCAL-1 promotes cell-cycle progression by interacting with and stabilizing galectin-1 in esophageal squamous cell carcinoma.
Yuanbo CuiMing YanWei WuPengju LvJinwu WangYanping HuoYanan LouXiwen MaJing ChangFang-Xia GuanWei CaoPublished in: NPJ precision oncology (2022)
Long non-coding RNAs (LncRNAs) play important roles in the development of human esophageal squamous cell carcinoma (ESCC). Our previous studies have shown that knockdown of LncRNA ESCCAL-1 expression inhibits the growth of ESCC cells, but the mechanisms remain largely unknown. In this study, we show that over-expression of ESCCAL-1 promotes ESCC cell proliferation and cell-cycle progression by blocking ubiquitin-mediated degradation of an oncoprotein galectin-1 (Gal-1). Multiple LncRNA expression datasets as well as our own data together reveal that ESCCAL-1 is evidently up-regulated in ESCC tissues and exhibits promising diagnostic value. Over-expression of ESCCAL-1 augmented ESCC cell proliferation and cell-cycle progression, whereas down-regulation of ESCCAL-1 resulted in the opposite effects. Mechanistically, LncRNA ESCCAL-1 directly binds to Gal-1 and positively regulates its protein level without affecting its mRNA level. Up-regulation of Gal-1 facilitated ESCC cell proliferation and cell-cycle progress. Knockdown of Gal-1 mitigated the effects of ESCCAL-1-mediated high cellular proliferation, NF-κB signaling activation and tumorigenicity of ESCC cells. Thus, our findings provide novel insight into the mechanism by which ESCCAL-1 facilitates ESCC tumorigenesis and cell-cycle progression by interacting with and stabilizing Gal-1 protein, suggesting a potential therapeutic target for ESCC.
Keyphrases
- cell cycle
- cell proliferation
- poor prognosis
- long non coding rna
- binding protein
- induced apoptosis
- pi k akt
- signaling pathway
- cell cycle arrest
- endothelial cells
- oxidative stress
- small molecule
- cell death
- genome wide
- dna methylation
- risk assessment
- machine learning
- single cell
- big data
- lps induced
- transcription factor
- artificial intelligence
- protein kinase
- rna seq
- network analysis
- toll like receptor
- human health
- pluripotent stem cells