G-protein-coupled receptors (GPCR) constitute the largest known superfamily for signal transduction and transmission, and they control a variety of physiological and pathological processes. GPCR adaptor b-arrestins (ARRBs) play a role in cancerous proliferation. However, the effect of ARRBs in inflammation-mediated hepatocellular carcinogenesis is unknown. Here we show that ARRB1, but not ARRB2, is upregulated in inflammation-associated hepatocellular carcinoma (HCC) and paracancerous tissues in humans. A genotoxic carcinogen, diethylnitrosamine (DEN), significantly induces hepatic inflammation, TNF-a production and ARRB1 expression. Although ARRB1 deficiency does not affect hepatic inflammation and TNF-a production, it markedly represses hepatocellular carcinogenesis by suppressing malignant proliferation in DEN-treated mice. Furthermore, TNF-a directly induces hepatic ARRB1 expression and enhances ARRB1 interaction with Akt by binding to boost Akt phosphorylation, resulting in malignant proliferation of liver cells. Our data suggest that ARRB1 enhances hepatocellular carcinogenesis by inflammation-mediated Akt signalling and that ARRB1 may be a potential therapeutic target for HCC.

Purpose Microvascular invasion (MVI) is a valuable predictor of survival in hepatocellular carcinoma (HCC) patients. This study developed predictive models using eXtreme Gradient Boosting (XGBoost) and deep learning based on CT images to predict MVI preoperatively. Methods In total, 405 patients were included. A total of 7302 radiomic features and 17 radiological features were extracted by a radiomics feature extraction package and radiologists, respectively. We developed a XGBoost model based on radiomics features, radiological features and clinical variables and a three-dimensional convolutional neural network (3D-CNN) to predict MVI status. Next, we compared the efficacy of the two models. Results Of the 405 patients, 220 (54.3%) were MVI positive, and 185 (45.7%) were MVI negative. The areas under the receiver operating characteristic curves (AUROCs) of the Radiomics-Radiological-Clinical (RRC) Model and 3D-CNN Model in the training set were 0.952 (95% confidence interval (CI) 0.923–0.973) and 0.980 (95% CI 0.959–0.993), respectively (p = 0.14). The AUROCs of the RRC Model and 3D-CNN Model in the validation set were 0.887 (95% CI 0.797–0.947) and 0.906 (95% CI 0.821–0.960), respectively (p = 0.83). Based on the MVI status predicted by the RRC and 3D-CNN Models, the mean recurrence-free survival (RFS) was significantly better in the predicted MVI-negative group than that in the predicted MVI-positive group (RRC Model: 69.95 vs. 24.80 months, p < 0.001; 3D-CNN Model: 64.06 vs. 31.05 months, p = 0.027). Conclusion The RRC Model and 3D-CNN models showed considerable efficacy in identifying MVI preoperatively. These machine learning models may facilitate decision-making in HCC treatment but requires further validation.

Epstein-Barr virus (EBV)-associated gastric carcinoma (EBVaGC) is a recently recognized entity, which is defined by the presence of EBV in the gastric carcinoma cells. EBVaGC represents about 10% of gastric carcinoma worldwide, and >80,000 patients are estimated to develop EBVaGC annually. EBVaGC shows some distinct clinicopathologic characteristics, such as male predominance, predisposition to the proximal stomach, and a high proportion in diffuse-type gastric carcinomas. Besides, EBVaGC also shows characteristic molecular abnormality, that is, global and nonrandom CpG-island methylation of the promoter region of many cancer-related genes, which causes downregulation of their expression. Moreover, EBVaGC has a relative favorable prognosis. The uniform presence of EBV-encoded small RNA in tumor cells but not in the surrounding normal epithelial cells, and the detection of monoclonal EBV episomes in EBVaGC, strongly suggests that EBV play an etiological role in gastric carcinogenesis. Therefore, EBVaGC should be regarded as a distinct entity of gastric carcinoma, although it only accounts for a relatively small fraction of total gastric carcinomas. In this review, the epidemiological and clinicopathologic features of EBVaGC and the genetic abnormalities of EBVaGC cell including chromosomal and epigenetic abnormalities are described. The roles of EBV in gastric carcinogenesis are discussed. We make an emphasis on the EBV latency pattern and genome polymorphisms as well as local immunity in EBVaGC. In addition, the treatment of EBVaGC is also briefly discussed. Taken together, this review aims to give the reader a full understanding of a newly defined entity of gastric carcinoma, EBVaGC.

SYK promotes liver fibrosis via activation of HSCs and is an attractive potential therapeutic target for liver fibrosis and prevention of HCC development. (Hepatology 2018).

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