PO-353 TIP60-dependent acetylation of SPZ1-TWIST complex promotes epithelial–mesenchymal transition and metastasis in liver cancer

Synthetic pyrrole-imidazole (PI) polyamides bind to the minor groove of double-helical DNA with high affinity and specificity, and inhibit the transcription of corresponding genes. In liver cancer, transforming growth factor (TGF)-β expression is correlated with tumor grade, and high-grade liver cancer tissues express epithelial-mesenchymal transition markers. TGF-β1 was reported to be involved in cancer development by transforming precancer cells to cancer stem cells (CSCs). This study aimed to evaluate the effects of TGF-β1-targeting PI polyamide on the growth of liver cancer cells and CSCs and their TGF-β1 expression. We analyzed TGF-β1 expression level after the administration of GB1101, a PI polyamide that targets human TGF-β1 promoter, and examined its effects on cell proliferation, invasiveness, and TGF-β1 mRNA expression level. GB1101 treatment dose-dependently decreased TGF-β1 mRNA levels in HepG2 and HLF cells, and inhibited HepG2 colony formation associated with downregulation of TGF-β1 mRNA. Although GB1101 did not substantially inhibit the proliferation of HepG2 cells compared to untreated control cells, GB1101 significantly suppressed the invasion of HLF cells, which displayed high expression of CD44, a marker for CSCs. Furthermore, GB1101 significantly inhibited HLF cell sphere formation by inhibiting TGF-β1 expression, in addition to suppressing the proliferation of HLE and HLF cells. Taken together, GB1101 reduced TGF-β1 expression in liver cancer cells and suppressed cell invasion; therefore, GB1101 is a novel candidate drug for the treatment of liver cancer.

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Value of Biomarkers in Liver Cancer EMT model under different interventions：A meta-analysis

10.21203/rs.3.rs-892562/v2 ◽

2021 ◽

Author(s):

Jing Yan ◽

Shuli Zou ◽

Bei Xie ◽

Ye Tian ◽

Zhiheng Peng ◽

...

Keyword(s):

Liver Cancer ◽

Epithelial Mesenchymal Transition ◽

Gene Knockout ◽

Meta Analysis ◽

China National Knowledge Infrastructure ◽

Mesenchymal Transition ◽

Tumor Microenvironments ◽

Genetic Intervention ◽

E Cadherin

Abstract Background There are various interventions to establish the Liver cancer epithelial-mesenchymal transition (EMT) models. However, the ideal biomarkers for unique model are not well established. Further studies are necessary to evaluation of effective EMT biomarkers under different interventions in vitro studies. A meta-analysis was performed to evaluate the performance of different biomarkers in HepG2 cells during EMT under multiple interventions. Methods PubMed, Web of Science, Embase, the China National Knowledge Infrastructure (CNKI), the China Biology Medicine disc (CBM), Wan Fang Data, and VIP databases were systematically searched from inception to June 14, 2020 by two independent reviewers. Results A total of 58 studies were included in the meta-analysis. Our study showed that E-cadherin responds well to the intervention of medication, genetic intervention, gene knockout/knockdown, hypoxia, and other tumor microenvironments, as well as non-coding RNA (ncRNA) overexpression and silencing. N-cadherin can effectively evaluate the intervention effect of medication, genetic intervention, hypoxia and other tumor microenvironments, as well as ncRNA overexpression. Vimentin reflects the effects of medication, pro-EMT genetic intervention and gene knockout/knockdown, anti-EMT ncRNA overexpression and anti-EMT ncRNA silencing and hypoxia. Snail only responds to the intervention of anti-EMT genetic intervention and gene knockout/knockdown, tumor microenvironments other than hypoxia, anti-EMT ncRNA overexpression and ncRNA silencing. Conclusions Our results shows that some medicine, some gene, microenvironment and some ncRNA can effectively induce/inhibit EMT process. E-cadherin, N-cadherin, Vimentin and Snail are effective biomarkers during this process. They respond differently to different intervention. Therefore, different biomarkers should be chosen under different intervention based on their performance.

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