Introduction: Hepatic malignancy is one of the most common malignant neoplasms around the globe, and hepatocellular carcinoma (HCC) is the most common type. In this study, the roles and mechanisms of MiRNA-610 in the chemo resistance of HCC will be discussed. Material and methods: The expression of MiRNA-610 and hepatoma-derived growth factor (HDGF) in HCC tissues and cell line was detected by quantitative real-time PCR. The proliferation and chemo resistance were analysed by MTT assay. Flow cytometry was used to examine the apoptosis rate. Luciferase reporter assay was used to verify the correlation between MiRNA-610 and HDGF. HDGF protein expression was detected by Western blot. Results: Our study confirmed the low-expression of MiRNA-610 in HCC tissues and cell line. Its low expression was related to high T stages and poor differentiation of HCC, and was a prognostic factor for HCC. MiRNA-610 upregulation inhibited cell proliferation and induced apoptosis of HepG2 cells. MiRNA-610 enhancement decreased the half maximal inhibitory concentration for cisplatin (DDP) and depressed the DDP resistance in HepG2 cells. The specific correlation between MiRNA-610 and HDGF was tested by luciferase reporter assay and western blot. The transfection with HDGF expression vector up-regulated the expression of HDGF protein silenced by MiRNA-610 enhancement. HDGF overexpression was found to reverse partly the regulatory roles of MiRNA-610 on malignancy and DDP resistance. Conclusions: MiRNA-610 not only played a tumour suppressor role in HCC but also affected chemo resistance to DDP. This role is mainly mediated through targeted silencing of the HDGF gene, which may offer a new potential therapeutic target and improve the clinical therapeutic effect for HCC.

Background The mortality rate of hepatocellular carcinoma (HCC) remains high worldwide despite surgery and chemotherapy. Immunotherapy is a promising treatment for the rapidly expanding HCC spectrum. Therefore, it is necessary to further explore the immune-related characteristics of the tumour microenvironment (TME), which plays a vital role in tumour initiation and progression. Methods In this research, 866 immune-related differentially expressed genes (DEGs) were identified by integrating the DEGs of samples from The Cancer Genome Atlas (TCGA)-HCC dataset and the immune-related genes from databases (InnateDB; ImmPort). Afterwards, 144 candidate prognostic genes were defined through weighted gene co-expression network analysis (WGCNA). Results Seven immune-related prognostic DEGs were identified using the L1-penalized least absolute shrinkage and selection operator (LASSO) Cox proportional hazards (PH) model, and the ImmuneRiskScore model was constructed on this basis. The prognostic index of the ImmuneRiskScore model was then validated in the relevant dataset. Patients were divided into high- and low-risk groups according to the ImmuneRiskScore. Differences in the immune cell infiltration of patients with different ImmuneRiskScore values were clarified, and the correlation of immune cell infiltration with immunotherapy biomarkers was further explored. Conclusion The ImmuneRiskScore of HCC could be a prognostic marker and can reflect the immune characteristics of the TME. Furthermore, it provides a potential biomarker for predicting the response to immunotherapy in HCC patients.

Background : The percentage of death resulted from hepatocellular carcinoma (HCC) remains high worldwide, despite surgical and chemotherapy treatment. Immunotherapy offers great promise in the treatment of a rapidly expanding spectrum of HCC. Therefore, further exploration of the immune-related signatures in the tumor microenvironment, which plays a vital role in tumor initiation and progression for immunotherapy is currently needed. Methods: In this present research, 866 immune-related difference expression genes (DEGs) were identified by integrating the DEGs between TCGA HCC and normal tissue and the immune genes from databases (Innate DB; Imm Port), and 144 candidate prognostic genes were defined through weighted gene co-expression network analysis (WGCNA). Results: Seven prognostic immune-related DEGs were determined by LASSO Cox PH model, which were followed by constructing the ImmuneRiskScore model based on the prognostic immune-related DEGs. The prognostic index of the ImmuneRiskScore was validated then in the dependent dataset. Patients were divided into high- and low-risk groups according to ImmuneRiskScore. The difference in ImmuneRiskScore and infiltration of immune cells between groups was detected and the correlation analysis for immunotherapy biomarkers was further explored. Conclusion: The ImmuneRiskScore of HCC could provide a prognostic signature and reflect immune characteristics within tumor microenvironment. Furthermore, it also may provide novel immunotherapy predictive biomarker for HCC patients in the near future.