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Chronic hepatitis B virus (HBV) infection is the major cause of hepatocellular carcinoma (HCC). The pre-S2 mutant large HBV surface antigen (LHBS) is highly associated with HCC. This study analyzed the expression of the large form of surface protein in tumors and evaluated the LHBS with mutations within the pre-S2 region as a high-risk recurrence marker in HCC patients after curative hepatic resection. By analyses using immunohistochemical staining (n = 12) and western blotting (n = 22), the HBV surface protein, which is mainly comprised of the major form of HBV surface antigen, was greatly diminished in the tumors. However, LHBS was not significantly decreased in tumorous regions, suggesting that LHBS maintains its expression in cancer development. A cohort of 175 patients with HBV-related HCC who underwent curative hepatic resection was analyzed for pre-S gene mutations using Pre-S Gene Chip. Results of the multivariate regression analysis showed that the serum pre-S2 mutant level and the American Joint Committee on Cancer stage were the two main independent high-risk factors for recurrence. A Cox proportional hazards analysis also revealed a prediction model, which indicated the recurrence-free survival rate along with the time after surgery; this was developed and further validated in an independent HCC cohort. Receiver operating characteristic curve analysis revealed that the model showed close sensitivities in the main and validation cohorts (area under the curve values, 0.741 and 0.704, respectively). Conclusion: Unlike the major HBV surface antigen, LHBS is mostly expressed in the tumorous regions of HBV-induced HCC, indicating that it plays a unique role in tumor progression; the relative level of pre-S2 mutant in serum is, independently of tumor stage, an important high-risk marker for HCC recurrence after primary hepatic resection. (Hepatology 2018).

AIM To investigate the clinicopathological significance of progesterone receptor membrane component 1 (PGRMC1) and PGRMC2 in hepatocellular carcinoma (HCC). METHODS We performed immunohistochemical staining to evaluate the estrogen receptor (ER), progesterone receptor (PR), PGRMC1, and PGRMC2 in a clinical cohort consisting of 89 paired HCC and non-tumor liver samples. We also analyzed HCC data (n = 373) from The Cancer Genome Atlas (TCGA). We correlated the expression status of PGRMC1 and PGRMC2 with clinicopathological indicators and the clinical outcomes of the HCC patients. We knocked down or overexpressed PGRMC1 in HCC cell lines to evaluate its biological significance in HCC cell proliferation, differentiation, migration, and invasion. RESULTS We found that few HCC cases expressed ER (5.6%) and PR (4.5%). In contrast, most HCC cases expressed PGRMC1 (89.9%) and PGRMC2 (100%). PGRMC1 and PGRMC2 exhibited significantly lower expression in tumor tissue than in non-tumor tissue (P < 0.001). Lower PGRMC1 expression in HCC was significantly associated with higher serum alpha-fetoprotein expression (P = 0.004), poorer tumor differentiation (P = 0.045) and liver capsule penetration (P = 0.038). Low PGRMC1 expression was an independent predictor for worse disease-free survival (P = 0.002, HR = 2.384, CI: 1.377-4.128) in our cases, as well as in the TCGA cohort (P < 0.001, HR = 2.857, CI: 1.781-4.584). The expression of PGRMC2 did not relate to patient outcome. PGRMC1 knockdown promoted a poorly differentiated phenotype and proliferation of HCC cells in vitro, while PGRMC1 overexpression caused the opposite effects. CONCLUSION PGRMC1 is a non-classical hormonal receptor that negatively regulates hepatocarcinogenesis. PGRMC1 down-regulation is associated with progression of HCC and is a poor prognostic indicator.

Because of rapid advancements in sequencing technology, the experimental platforms of RNA-seq are updated frequently. It is quite common to combine data sets from several experimental platforms for analysis in order to increase the sample size and achieve more powerful tests for detecting the presence of differential gene expression. The data sets combined from different experimental platforms will have a complex data distribution, which causes a major problem in statistical modeling as well as in multiple testing. Although plenty of research have studied this problem by modeling the batch effects, there are no general and robust data-driven procedures for RNA-seq analysis. In this paper we propose a new robust procedure which combines the use of popular methods (packages) with a data-driven simulation (DDS). We construct the average receiver operating characteristic curve through the DDS to provide the calibrated levels of significance for multiple testing. Instead of further modifying the adjusted p-values, we calibrated the levels of significance for each specific method and mean effect model. The procedure was demonstrated with several popular RNA-seq analysis methods (edgeR, DEseq2, limma+voom). The proposed procedure relaxes the stringent assumptions of data distributions for RNA-seq analysis methods and is illustrated using colorectal cancer studies from The Cancer Genome Atlas database.

Ground glass hepatocytes (GGHs) have been shown to predict the development of hepatocellular carcinoma (HCC). Type I GGH and type II GGH harbor hepatitis B virus (HBV) pre-S1 and pre-S2 deletion mutants, respectively. Whether anti-HBV therapy can inhibit the expression of GGHs and potentially reduce HCC development is explored in this study. Two sets of liver specimens were included: the first contained 31 paired biopsy specimens obtained from chronic HBV patients receiving oral nucleos(t)ide analogue (NA) treatment; the second contained 186 resected liver tissues obtained from HBV-related HCC patients receiving surgery: 82 received NA before surgery and 104 did not. Compared with the baseline biopsy specimens, type I (P=0.527) and type II GGH (P=0.077) were not significantly decreased after 48 weeks of NA treatment in the first set of patients. In the second set, despite suppression of viral load (P

Background Surgery is the potentially curative treatment for retroperitoneal sarcoma (RS), but complete resectability is frequently a challenge. This study aimed to characterize the clinical features, prognostic factors and treatment outcomes. Methods A cohort of 144 patients with RS was surveyed retrospectively from January 1st, 2000 to July 30th, 2011. The prognostic influence of clinicopathological characteristics as well as treatments on local recurrence-free survival (LRFS), distant metastasis-free survival (DMFS), and overall survival (OS), were examined by univariate and multivariate analyses. A histology-specific nomogram developed by Gronchi et al was used for validation. Results Liposarcoma, leiomyosarcoma, and malignant peripheral sheath tumor (MPNST) were the most common histologies (70%). Multivariate analysis revealed FNCLCC tumor grade was the most significant prognostic factor for OS (P = 0.001) and DMFS (P