LINC00857 contributes to hepatocellular carcinoma malignancy via enhancing epithelial-mesenchymal transition.
Chaofeng XiaXiao-Yu ZhangWenhui LiuMan JuYingdong JuYan-Zhi BuWeixing WangHongjin ShaoPublished in: Journal of cellular biochemistry (2018)
Hepatocellular carcinoma (HCC) remains the fifth most frequent cancer with high mortality rate worldwide. However, the underlying molecular mechanisms of HCC progression are still barely known. Long noncoding RNAs (lncRNAs) have been recognized as significant therapeutic targets for HCC. Recently, the biological role of LINC00857 in several cancer types has been reported. Our present study was aimed to investigate the role of LINC00857 in HCC progression. We observed that LINC00857 was overexpressed in HCC cell lines (Huh7, Hep3B, HepG2, MHCC-97H, and SNU449). Knockdown of LINC00857 significantly repressed Hep-3B and SNU449 cell proliferation and inhibited the HCC cell colony formation. In addition, cell apoptosis was induced by the silence of LINC00857 and cell cycle progression was blocked in G1 phase. Besides these, downregulation of LINC00857 was able to restrain HCC cell migration and invasion capacity via enhancing epithelial-mesenchymal transition (EMT) process. As displayed, E-cadherin protein expression was increased by LINC00857 silence, while N-cadherin protein level was repressed by LV-shLINC00857 in HCC cells. Finally, the in vivo assays were used and the data indicated that LINC00857 could also obviously suppress the HCC tumor growth in vivo. In conclusion, our study revealed that LINC00857 might provide a novel perspective for the HCC treatment.
Keyphrases
- cell proliferation
- long non coding rna
- cell cycle
- long noncoding rna
- epithelial mesenchymal transition
- pi k akt
- single cell
- papillary thyroid
- machine learning
- induced apoptosis
- type diabetes
- squamous cell carcinoma
- cardiovascular events
- risk factors
- stem cells
- oxidative stress
- artificial intelligence
- big data
- cell cycle arrest
- coronary artery disease
- transcription factor
- data analysis
- squamous cell