Inhibition of UBA52 induces autophagy via EMC6 to suppress hepatocellular carcinoma tumorigenesis and progression.
Li TongXiaofei ZhengTianqi WangWang GuTingting ShenWenkang YuanSiyu WangSonglin XingXiaoying LiuChong ZhangChao ZhangPublished in: Journal of cellular and molecular medicine (2024)
Ubiquitin A-52 residue ribosomal protein fusion product 1 (UBA52) has a role in the occurrence and development of tumours. However, the mechanism by which UBA52 regulates hepatocellular carcinoma (HCC) tumorigenesis and progression remains poorly understood. By using the Cell Counting Kit (CCK-8), colony formation, wound healing and Transwell assays, we assessed the effects of UBA52 knockdown and overexpression on the proliferation and migration of HCC cells in vitro. By establishing subcutaneous and metastatic tumour models in nude mice, we evaluated the effects of UBA52 on HCC cell proliferation and migration in vivo. Through bioinformatic analysis of data from the Gene Expression Profiling Interactive Analysis (GEPIA) and The Cancer Genome Atlas (TCGA) databases, we discovered that UBA52 is associated with autophagy. In addition, we discovered that HCC tissues with high UBA52 expression had a poor prognosis in patients. Moreover, knockdown of UBA52 reduced HCC cell growth and metastasis both in vitro and in vivo. Mechanistically, knockdown of UBA52 induced autophagy through EMC6 in HCC cells. These findings suggest that UBA52 promoted the proliferation and migration of HCC cells through autophagy regulation via EMC6 and imply that UBA52 may be a viable novel treatment target for HCC patients.
Keyphrases
- poor prognosis
- induced apoptosis
- endoplasmic reticulum stress
- cell death
- cell cycle arrest
- end stage renal disease
- single cell
- signaling pathway
- oxidative stress
- chronic kidney disease
- ejection fraction
- newly diagnosed
- long non coding rna
- small cell lung cancer
- genome wide
- squamous cell carcinoma
- prognostic factors
- gene expression
- young adults
- cell proliferation
- high throughput
- metabolic syndrome
- binding protein
- machine learning
- dna methylation
- wound healing
- bone marrow
- type diabetes
- big data
- electronic health record
- diabetic rats
- insulin resistance
- adipose tissue
- patient reported
- protein protein
- childhood cancer