Cosmc overexpression enhances malignancies in human colon cancer.
Tianbo GaoTan DuXin HuXichen DongLina LiYakun WangJian LiuLijie LiuTao GuTao WenPublished in: Journal of cellular and molecular medicine (2019)
Cosmc is known as a T-synthase-specific molecular chaperone that plays a crucial role in the process of O-glycosylation. Cosmc dysfunction leads to inactive T-synthase and results in aberrant O-glycosylation, which is associated with various tumour malignancies. However, it is unclear whether Cosmc has some other functions beyond its involvement in O-glycosylation. In this study, we aimed to investigate the functional role of Cosmc in human colorectal cancer (CRC). We first assessed the expression levels of Cosmc in human CRC specimens and then forcedly expressed Cosmc in human CRC cell lines (HCT116, SW480) to examine its impact on cellular behaviours. The mechanisms for aberrant expression of Cosmc in CRC tissues and the altered behaviours of tumour cells were explored. It showed that the mRNA and protein levels of Cosmc were markedly elevated in human CRC specimens relative to normal colorectal tissues. The occurrence of endoplasmic reticulum (ER) stress may largely contribute to the increased Cosmc expression in cancer tissue and cells. Cosmc overexpression in CRC cells significantly promoted cell migration and invasion, which could be attributed to the activation of the epithelial-mesenchymal transition (EMT) pathway rather than aberrant O-glycosylation. These data indicate that Cosmc expression was elevated in human CRC possibly caused by ER stress, which further enhanced malignancies through the activation of EMT but independently of aberrant O-glycosylation.
Keyphrases
- endothelial cells
- epithelial mesenchymal transition
- poor prognosis
- induced pluripotent stem cells
- induced apoptosis
- gene expression
- stem cells
- squamous cell carcinoma
- endoplasmic reticulum
- binding protein
- risk assessment
- transcription factor
- endoplasmic reticulum stress
- signaling pathway
- mesenchymal stem cells
- small molecule
- big data
- deep learning