Background & Aims: Apoptosis mediated by p53 plays a pathological role in the progression of hepatosteatosis. It is noteworthy that p53 can promote the expression of damage‐regulated autophagy modulator (DRAM), an inducer of autophagy‐mediated apoptosis. However, the relationship between p53‐mediated apoptosis and autophagy in hepatosteatosis remains elusive. This study aimed to examine how p53 orchestrates autophagy and apoptosis to affect hepatosteatosis.Methods: HepG2 cells were treated with oleic acid (OA) for 24 h to induce hepatosteatosis. Mice were fed a high‐fat diet for 20 or 40 weeks to induce hepatosteatosis.Results: OA induced a dose‐dependent increase in steatosis severity and apoptosis. OA also induced autophagy, which was a critical inducer of apoptosis in mild steatosis induced by 400 μM OA, but not in the more severe steatosis induced by 800 and 1200 μM OA. p53 inhibition by siRNA mostly blocked OA‐induced apoptosis and autophagy. Moreover, OA‐induced autophagy was DRAM‐dependent and primarily occurred in the mitochondria (mitophagy), where DRAM was localized. In severe steatosis induced by 1200 μM OA, apoptosis was mainly dependent on p53‐induced expression of BAX, which was also localized to the mitochondria. Our in vivo study showed that p53 expression increased in both mild and severe hepatosteatosis. Increased DRAM expression and autophagy were identified in mild hepatosteatosis, whereas greater BAX expression was observed in severe hepatosteatosis.Conclusions: p53 may induce apoptosis via different mechanisms. DRAM‐mediated mitophagy is a primary apoptotic inducer in mild hepatosteatosis, whereas p53‐induced BAX expression mainly induces apoptosis in severe hepatosteatosis.

Background: Hepatocellular carcinoma (HCC) is one of the most prevalent cancers worldwide. However, the precise mechanisms of the development and progression of HCC remain unclear. The present study attempted to identify and functionally analyze the differentially expressed genes between HCC and cirrhotic tissues by using comprehensive bioinformatics analyses. Methods: The GSE63898 gene expression profile was downloaded from the Gene Expression Omnibus (GEO) and analyzed using the online tool GEO2R to identify differentially expressed genes (DEGs). Gene ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEGs were performed in DAVID. The STRING database was used to evaluate the interactions of DEGs and to construct a protein-protein interaction (PPI) network using Cytoscape software. Hub genes were selected using the cytoHubba plugin and were validated with the cBioPortal database. Results: A total of 301 DEGs were identified between HCC and cirrhotic tissues. The GO analysis results showed that these DEGs were significantly enriched in certain biological processes including negative regulation of growth and cell chemotaxis. Several significant pathways, including the p53 signaling pathway, were identified as being closely associated with these DEGs. The top 12 hub genes were screened and included TTK, NCAPG, TOP2A, CCNB1, CDK1, PRC1, RRM2, UBE2C, ZWINT, CDKN3, AURKA, and RACGAP1. The cBioPortal analysis found that alterations in hub genes could result in significantly reduced disease-free survival in HCC. Conclusion: The present study identified a series of key genes and pathways that may be involved in the tumorigenicity and progression of HCC, providing a new understanding of the underlying molecular mechanisms of carcinogenesis in HCC.

Objective: This study aimed to evaluate the individual and combined diagnostic values of serum alpha-fetoprotein (AFP), des-gamma-carboxyprothrombin (DCP), glypican-3 (GPC3) and golgi protein 73 (GP73) in diagnosing hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). Methods: Participants from Beijing YouAn Hospital were enrolled and divided into seven groups. Serum was collected and the levels of AFP, GPC3, GP73 and DCP were simultaneously measured with a protein array. Pearson’s χ² test was applied to compare the clinicopathological characteristics. Receiver operating characteristic (ROC) curves were used to analyse the diagnostic performance of the four markers. Results: As a single biomarker for differentiating HCC from all controls, AFP had a larger area under the curve (AUC) (0.798, 95% CI (0.754– 0.838) than the other biomarkers, with a sensitivity of 77.3% and a specificity of 71.1%. Among the other combinations, AFP plus GPC3 and DCP (0.871, 95% CI (0.833– 0.903)) was the best at differentiating HCC from all controls. In discriminating very early stage and early stage HCC from all controls, the AUC of GPC3 (0.744, 95% CI (0.690– 0.793); sensitivity 62.8%; specificity 83.3%) was better than that of AFP (0.723, 95% CI (0.668– 0.774); sensitivity 67.3%; specificity 71.7%). Among all biomarker combinations, the combination of AFP, GPC3 and GP73 had the largest AUC (0.843, 95% CI (0.796– 0.883); sensitivity 84.1%; specificity 71.7%). AFP (AUC 0.726, 95% CI (0.662– 0.784)) showed the best performance in the very early diagnosis of HBV-related HCC. Conclusion: As a single biomarker, AFP has an advantage in the very early and early diagnosis of HBV-related HCC. The combination of AFP, GPC3 and GP73 is the most suitable marker for the early diagnosis of HBV-related HCC. However, AFP remains the best biomarker for the very early diagnosis of HBV-related HCC, and the adding of one or more markers does not significantly improve the diagnostic accuracy.

Background: Hepatitis B virus (HBV)-related liver disease induces liver damage by hepatic immune and inflammatory response. The association between aberrant peripheral blood mononuclear cell (PBMC) DNA methylation and progression of liver disease and fibrosis remains unclear. Results: Here we applied Infinium 450 K BeadChip investigating PBMC genome-wide methylation profiling of 48 HBV-related liver disease patients including 24 chronic hepatitis B (CHB), 14 compensated liver cirrhosis (LC), and 10 decompensated liver cirrhosis (DLC). In total, there were 7888 differentially methylated CpG sites (36.06% hypermethylation, 63.94% hypomethylation) correlate with liver disease progression. LC was difficult to be diagnosed, intermediating between CHB and DLC. We used least absolute shrinkage and selection operator (LASSO)-logistic regression method to perform a LC predictive model. The predicted probability (P) of having LC was estimated by the combined model: P = 1/(1 − e^−x), where X = 11.52 − 2.82 × (if AST within the normal range − 0.19 × (percent methylation of cg05650055) − 0.21 × (percent methylation of cg17149911 ). Pyrosequencing validation and confusion matrix analysis was used for internal testing, area under receiver operating characteristic curve (AUROC) of model was 0.917 (95% CI, 0.80–0.977). On the fibrosis progress, there were 1705 genes in LC compared with CHB, whose differentially methylated CpG sites loading within the “promoter” regions (including TSS1500, TSS200, 5′UTR, and the 1st exon of genes) subject into the enrichment analysis using Ingenuity Pathway Analysis (IPA). There were 113 enriched immune-related pathways indicated that HBV-related liver fibrosis progression caused epigenetic reprogramming of the immune and inflammatory response. Conclusions: These data support idea that development of HBV-related chronic liver disease is linked with robust and broad alteration of methylation in peripheral immune system. CpG methylation sites serve as relevant biomarker candidates to monitor and diagnose LC, providing new insight into the immune mechanisms understanding the progression of HBV-related liver fibrosis and cirrhosis.

Objectives: Des-gamma-carboxyprothrombin (DCP) is an important serum biomarker for the clinical screening of hepatocellular carcinoma (HCC). This study aimed to evaluate the value of DCP for the early diagnosis of alpha-fetoprotein (AFP)-negative hepatitis B virus (HBV)-related HCC. Methods: We retrospectively enrolled patients with AFP-negative HBV-related HCC and benign liver disease. Serum DCP levels in all participants were measured by chemiluminescent enzyme immunoassay. The value of DCP for the early diagnosis of AFP-negative HBV-related HCC was evaluated by receiver operating characteristic curve (ROC) and area under the curve (AUC) analyses. Results: A total of 210 patients, including 87 cases with AFP-negative HBV-related HCC and 123 control cases with chronic HBV infection (CHB) or liver cirrhosis (LC), were included. Serum DCP levels were significantly increased in patients with AFP-negative HBV-related HCC compared with those with CHB or LC. The AUC for DCP for distinguishing between the two groups was 0.731 (95% confidence interval (CI), 0.657 to 0.805) and that for early diagnosis was 0.685 (95%CI, 0.596 to 0.774). Conclusions: DCP may be a favorable biomarker to improve the early diagnosis rate of AFP-negative HBV-related HCC.

The adoption of the electronic medical record (EMR) is rapidly growing in China. Constantly evolving, Chinese EMRs contain vast amounts of clinical and financial data, providing tremendous potential for research and policy use; however, they are only partially standardized and contain free text or unstructured data. To utilize the information contained in Chinese EMRs, the development of data extraction methodology is urgently needed. The purpose of this study is to develop and validate methods to extract clinical information from the Chinese EMR for research use. Using 2010 to 2014 EMR data from YouAn Hospital, a large teaching hospital affiliated with Capital Medical University in Beijing, China, we developed extraction methods including 40 EMR definitions for defining 6 liver disease, 5 disease severity conditions, and 29 comorbidities and treatments. We conducted a chart review of 450 randomly selected EMRs. Using physician chart review results as a reference, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated to validate each EMR definition. The sensitivity of the 6 EMR definitions for liver diseases ranged from 78.9 to 100.0 %, and PPV ranged from 82.1 to 100.0 %. The sensitivity of the 5 definitions on disease severity conditions ranged from 91.0 to 100.0 %, and PPV ranged from 79.2 to 100.0 %. Among the 29 EMR definitions for comorbidities and treatments, 23 had sensitivity over 90.0 % and 25 had PPV over 80.0 %. The specificity and NPV for all 40 EMR definitions were over 90.0 %. The extraction method developed is a valid way of extracting information on liver diseases, comorbidities and related treatments from YouAn hospital EMRs. Our method should be modified for application to other Chinese EMR systems, following our framework for extracting conditions.