Dataset Information

High glucose induced c-Met activation promotes aggressive phenotype and regulates expression of glucose metabolism genes in HCC cells.

ABSTRACT: Hepatocellular carcinoma (HCC) is strongly associated with metabolic dysregulations/deregulations and hyperglycemia is a common metabolic disturbance in metabolic diseases. Hyperglycemia is defined to promote epithelial to mesenchymal transition (EMT) of cancer cells in various cancers but its molecular contribution to HCC progression and aggressiveness is relatively unclear. In this study, we analyzed the molecular mechanisms behind the hyperglycemia-induced EMT in HCC cell lines. Here, we report that high glucose promotes EMT through activating c-Met receptor tyrosine kinase via promoting its ligand-independent homodimerization. c-Met activation is critical for high glucose induced acquisition of mesenchymal phenotype, survival under high glucose stress and reprogramming of cellular metabolism by modulating glucose metabolism gene expression to promote aggressiveness in HCC cells. The crucial role of c-Met in high glucose induced EMT and aggressiveness may be the potential link between metabolic syndrome-related hepatocarcinogenesis and/or HCC progression. Considering c-Met inhibition in hyperglycemic patients would be an important complementary strategy for therapy that favors sensitization of HCC cells to therapeutics.

SUBMITTER: Topel H

PROVIDER: S-EPMC8166976 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundHepatocellular carcinoma (HCC) is one of the most common cancers worldwide, with a high mortality and poor survival rate. Abnormal tumor metabolism is considered a hallmark of HCC and is a potential therapeutic target. This study aimed to identify metabolism-related biomarkers to evaluate the prognosis of patients with HCC.MethodThe Cancer Genome Atlas (TCGA) database was used to explore differential metabolic pathways based on high and low epithelial-mesenchymal transition (EMT) groupings. Genes in differential metabolic pathways were obtained for HCC metabolism-related molecular subtype analysis. Differentially expressed genes (DEGs) from the three subtypes were subjected to Lasso Cox regression analysis to construct prognostic risk models. Stard5 expression in HCC patients was detected by western blot and immunohistochemistry (IHC), and the role of Stard5 in the metastasis of HCC was investigated by cytological experiments.ResultsUnsupervised clustering analysis based on metabolism-related genes revealed three subtypes in HCC with differential prognosis. A risk prognostic model was constructed based on 11 genes (STARD5, FTCD, SCN4A, ADH4, CFHR3, CYP2C9, CCL14, GADD45G, SOX11, SCIN, and SLC2A1) obtained by LASSO Cox regression analysis of the three subtypes of DEGs. We validated that the model had a good predictive power. In addition, we found that the high-risk group had a poor prognosis, higher proportion of Tregs, and responded poorly to chemotherapy. We also found that Stard5 expression was markedly decreased in HCC tissues, which was associated with poor prognosis and EMT. Knockdown of Stard5 contributed to the invasion and migration of HCC cells. Overexpression of Stard5 inhibited EMT in HCC cells.ConclusionWe developed a new model based on 11 metabolism-related genes, which predicted the prognosis and response to chemotherapy or immunotherapy for HCC. Notably, we demonstrated for the first time that Stard5 acted as a tumor suppressor by inhibiting metastasis in HCC.

Dataset Information

High glucose induced c-Met activation promotes aggressive phenotype and regulates expression of glucose metabolism genes in HCC cells.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets