Identification of a liver cirrhosis signature in plasma for predicting hepatocellular carcinoma risk in a population-based cohort of hepatitis B carriers

Hepatitis B virus suppresses the secretion of insulin-like growth factor binding protein 1 to facilitate anti-apoptotic IGF-1 effects in HepG2 cells

Hepatitis B virus (HBV) infection is a major global health burden as chronic hepatitis B (CHB) is associated with the development of liver diseases including hepatocellular carcinoma (HCC). To gain insight into the mechanisms causing HBV-related HCC, we investigated the effects of HBV replication on global host cell gene expression using human HepG2 liver cells. By microarray analysis, we identified 54 differentially expressed genes in HBV-replicating HepG2 cells. One of the differentially-expressed genes was insulin-like growth factor binding protein 1 (IGFBP1) which was downregulated in HBV-replicating cells. Consistent with the gene expression data, IGFBP1 was suppressed at both the cellular and secreted protein levels in the presence of HBV replication. Transient transfection experiments with an inducible plasmid encoding the HBV X protein (HBx) revealed that HBx alone was sufficient to modulate IGFBP1 expression. Small interference RNA (siRNA)-mediated loss of function studies revealed that knockdown of IGFBP1 reduced apoptosis induced by either thapsigargin (TG) or staurosporine (STS). Treatment of cells with recombinant insulin-like growth factor 1 (IGF-1) decreased both TG- or STS-induced apoptosis. Interestingly, addition of recombinant IGFBP1 reversed the anti-apoptotic effect of IGF-1 on TG-induced, but not STS-induced, apoptosis. In conclusion, our results suggest an anti-apoptotic autocrine function of HBV-mediated downregulation of IGFBP1 in HepG2 cells. Such an effect may contribute to the development of HBV-mediated HCC by increasing pro-survival and anti-apoptotic IGF-1 effects.

INDEED: Integrated differential expression and differential network analysis of omic data for biomarker discovery

Differential expression (DE) analysis is commonly used to identify biomarker candidates that have significant changes in their expression levels between distinct biological groups. One drawback of DE analysis is that it only considers the changes on single biomolecule level. Recently, differential network (DN) analysis has become popular due to its capability to measure the changes on biomolecular pair level. In DN analysis, network is typically built based on correlation and biomarker candidates are selected by investigating the network topology. However, correlation tends to generate over-complicated networks and the selection of biomarker candidates purely based on network topology ignores the changes on single biomolecule level. In this paper, we propose a novel approach, INDEED, that builds sparse differential network based on partial correlation and integrates DE and DN analyses for biomarker discovery. We applied this approach on real proteomic and glycomic data generated by liquid chromatography coupled with mass spectrometry for hepatocellular carcinoma (HCC) biomarker discovery study. For each omic data, we used one dataset to select biomarker candidates, built a disease classifier and evaluated the performance of the classifier on an independent dataset. The biomarker candidates, selected by INDEED, were more reproducible across independent datasets, and led to a higher classification accuracy in predicting HCC cases and cirrhotic controls compared with those selected by separate DE and DN analyses. INDEED also identified some candidates previously reported to be relevant to HCC, such as intercellular adhesion molecule 2 (ICAM2) and c4b-binding protein alpha chain (C4BPA), which were missed by both DE and DN analyses. In addition, we applied INDEED for survival time prediction based on transcriptomic data acquired by analysis of samples from breast cancer patients. We selected biomarker candidates and built a regression model for survival time prediction based on a gene expression dataset and patients' survival records. We evaluated the performance of the regression model on an independent dataset. Compared with the biomarker candidates selected by DE and DN analyses, those selected through INDEED led to more accurate survival time prediction.

Diagnostic and therapeutic management of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is an increasing health problem, representing the second cause of cancer-related mortality worldwide. The major risk factor for HCC is cirrhosis. In developing countries, viral hepatitis represent the major risk factor, whereas in developed countries, the epidemic of obesity, diabetes and nonalcoholic steatohepatitis contribute to the observed increase in HCC incidence. Cirrhotic patients are recommended to undergo HCC surveillance by abdominal ultrasounds at 6-mo intervals. The current diagnostic algorithms for HCC rely on typical radiological hallmarks in dynamic contrast-enhanced imaging, while the use of α-fetoprotein as an independent tool for HCC surveillance is not recommended by current guidelines due to its low sensitivity and specificity. Early diagnosis is crucial for curative treatments. Surgical resection, radiofrequency ablation and liver transplantation are considered the cornerstones of curative therapy, while for patients with more advanced HCC recommended options include sorafenib and trans-arterial chemo-embolization. A multidisciplinary team, consisting of hepatologists, surgeons, radiologists, oncologists and pathologists, is fundamental for a correct management. In this paper, we review the diagnostic and therapeutic management of HCC, with a focus on the most recent evidences and recommendations from guidelines.

Epigenetic biomarkers in liver cancer

Liver cancer (hepatocellular carcinoma or HCC) is a major cancer worldwide. Research in this field is needed to identify biomarkers that can be used for early detection of the disease as well as new approaches to its treatment. Epigenetic biomarkers provide an opportunity to understand liver cancer etiology and evaluate novel epigenetic inhibitors for treatment. Traditionally, liver cirrhosis, proteomic biomarkers, and the presence of hepatitis viruses have been used for the detection and diagnosis of liver cancer. Promising results from microRNA (miRNA) profiling and hypermethylation of selected genes have raised hopes of identifying new biomarkers. Some of these epigenetic biomarkers may be useful in risk assessment and for screening populations to identify who is likely to develop cancer. Challenges and opportunities in the field are discussed in this chapter.

Current biomarkers for hepatocellular carcinoma: Surveillance, diagnosis and prediction of prognosis

Biomarkers for surveillance, diagnosis and prediction of prognosis in patients with hepatocellular carcinoma (HCC) are currently not ready for introduction into clinical practice because of limited sensitivity and specificity. Especially for the early detection of small HCC novel biomarkers are needed to improve the current effectiveness of screening performed by ultrasound. The use of high-throughput technologies in hepatocellular research allows to identify molecules involved in the complex pathways in hepatocarcinogenesis. Several invasive and non-invasive biomarkers have been identified already and have been evaluated in different clinical settings. Gene signatures with prognostic potential have been identified by gene expression profiling from tumor tissue. However, a single "all-in-one" biomarker that fits all-surveillance, diagnosis, prediction of prognosis-has not been found so far. The future of biomarkers most probably lies in a combination of non-invasive biomarkers, imaging and clinical parameters in a surveillance setting. Molecular profiling of tumorous and non-tumorous liver tissue may allow a prediction of prognosis for the individual patient and hopefully clear the way for individual treatment approaches. This article gives an overview on current developments in biomarker research in HCC with a focus on currently available and novel biomarkers, in particular on microRNA.

Hepatoprotective effects of loach (Misgurnus anguillicaudatus) lyophilized powder on dimethylnitrosamine-induced liver fibrosis in rats

This study investigates the hepatoprotective effects and the potential therapeutic mechanisms of loach (Misgurnus anguillicaudatus) lyophilized powder (MLP) on dimethylnitrosamine (DMN) induced liver fibrosis in rats. After treatment with MLP (50, 100, 200 mg/kg), alanine aminotransferase (ALT), aspartate aminotransferase (AST), albumin (Alb), total protein (TP) and hydroxyproline (Hyp) levels were detected, to assess the destruction of hepatocytes and the extent of liver fibrosis. Matrix metalloproteinase-2 (MMP-2), matrix metalloproteinase-9 (MMP-9), hyaluronic acid (HA), Laminin (LN), procollagen type-III (PC-III), collagen type-IV (C-IV), and transforming growth factor-β₁ (TGF-β₁) contents in serum were all tested using ELISA kits. Alpha-smooth muscle actin (α-SMA) and the tissue inhibitor of metalloproteinase-1 (TIMP-1) protein contents and distribution were evaluated using western blot and immunohistochemical analysis. MLP significantly decreased the serum concentrations of ALT, AST, Hyp, HA, LN, PC-III, C-IV, MMP-2, TIMP-1, α-SMA and TGF-β₁, while increasing the contents of Alb and MMP-9. No significant changes on TP serum concentrations were observed. These results suggest that MLP has anti-hepatic fibrosis effects and its mechanism may be associated with the attenuation of extracellular matrix (ECM) synthesis, the acceleration of ECM degradation, inhibition of hepatic stellate cells (HSCs) activation and TGF-β₁ expression.