Inhibition of the transient receptor potential vanilloid 3 channel attenuates carbon tetrachloride-induced hepatic fibrosis.

Transient receptor potential vanilloid 3 (TRPV3) is a member of the TRP superfamily. Previous studies have demonstrated that TRPV3 is associated with myocardial fibrosis. However, the role of TRPV3 in hepatic fibrosis and its underlying mechanisms are still unclear. This study aimed to elucidate the underlying effects of TRPV3 on hepatic fibrosis at multiple biological levels. First, immunohistochemical staining was performed to examine TRPV3 expression in human hepatic cirrhosis tissues. Then, we established a CCl4-induced hepatic fibrosis mouse model. The TRPV3 selective agonist drofenine and its inhibitor, forsythoside B, were intraperitoneally injected to investigate the relationship between TRPV3 and liver fibrosis progression. Finally, in vitro studies were performed using hepatic stellate cells (HSCs) to discover the potential molecular biological mechanisms. Immunohistochemistry revealed TRPV3 overexpression in liver cirrhosis. In the liver fibrosis groups, TRPV3 inhibitor treatment significantly reduced liver fibrosis, while TRPV3 agonist exacerbated its progression. In HSCs, knocking down TRPV3 with siRNA impaired DNA synthesis and cell proliferation and increased cell apoptosis. Furthermore, we found that knockdown of TRPV3 could reduce the lectin like oxidized lowdensity lipoprotein receptor-1 (LOX-1) protein levels. Our research suggests that lower expression or functional levels of TRPV3 can ameliorate the inflammatory response and fibrotic tissue proliferation.

Identification and validation of immune-related gene signature models for predicting prognosis and immunotherapy response in hepatocellular carcinoma.

Background: This study seeks to enhance the accuracy and efficiency of clinical diagnosis and therapeutic decision-making in hepatocellular carcinoma (HCC), as well as to optimize the assessment of immunotherapy response. Methods: A training set comprising 305 HCC cases was obtained from The Cancer Genome Atlas (TCGA) database. Initially, a screening process was undertaken to identify prognostically significant immune-related genes (IRGs), followed by the application of logistic regression and least absolute shrinkage and selection operator (LASSO) regression methods for gene modeling. Subsequently, the final model was constructed using support vector machines-recursive feature elimination (SVM-RFE). Following model evaluation, quantitative polymerase chain reaction (qPCR) was employed to examine the gene expression profiles in tissue samples obtained from our cohort of 54 patients with HCC and an independent cohort of 231 patients, and the prognostic relevance of the model was substantiated. Thereafter, the association of the model with the immune responses was examined, and its predictive value regarding the efficacy of immunotherapy was corroborated through studies involving three cohorts undergoing immunotherapy. Finally, the study uncovered the potential mechanism by which the model contributed to prognosticating HCC outcomes and assessing immunotherapy effectiveness. Results: SVM-RFE modeling was applied to develop an OS prognostic model based on six IRGs (CMTM7, HDAC1, HRAS, PSMD1, RAET1E, and TXLNA). The performance of the model was assessed by AUC values on the ROC curves, resulting in values of 0.83, 0.73, and 0.75 for the predictions at 1, 3, and 5 years, respectively. A marked difference in OS outcomes was noted when comparing the high-risk group (HRG) with the low-risk group (LRG), as demonstrated in both the initial training set (P <0.0001) and the subsequent validation cohort (P <0.0001). Additionally, the SVMRS in the HRG demonstrated a notable positive correlation with key immune checkpoint genes (CTLA-4, PD-1, and PD-L1). The results obtained from the examination of three cohorts undergoing immunotherapy affirmed the potential capability of this model in predicting immunotherapy effectiveness. Conclusions: The HCC predictive model developed in this study, comprising six genes, demonstrates a robust capability to predict the OS of patients with HCC and immunotherapy effectiveness in tumor management.