Noninvasive assessment of severity of liver fibrosis is crucial for understanding histology and making decisions on antiviral treatment for chronic HBV in view of the associated risks of biopsy. We aimed to develop a computer-assisted assessment system for the evaluation of liver disease severity by using machine leaning classifier based on physical-layer with serum markers. The retrospective data set, including 920 patients, was used to establish Decision Tree Classifier (DTC), Random Forest Classifier (RFC), Logistic Regression Classifier (LRC), and Support Vector Classifier (SVC) for liver fibrosis severity assessment. Training and testing samples account for 50% of the data set, respectively. The best indicator combinations were selected in random combinations of 24 indicators including 67 108 760 group indicators by four different machine learning classifiers. The resulting classifiers prospectively tested in 50% testing patients, and the sensitivity, specificity, overall accuracy, and receiver operating characteristics (ROC) were used to compare four classifiers to existed 19 models. Results show that the RFC-based classifier system, with 9 indicators, is feasible to assess severity for liver fibrosis with diagnostic accuracy (greater than 0.83) superior to existing 19 models. Additional studies based on a large data set with full serum markers and imaging information are necessary to enhance diagnostic accuracy and to expand clinical application.

Approx. 4% of patients experiencing chronic infection of human HCV (hepatitis C virus) ultimately develop HCC (hepatocellular carcinoma). The NS5A (non-structural protein 5A) encoded by HCV has been reported to have an oncogenic role during HCV infection, but the precise mechanism remains largely unclear. The aim of this study is to investigate the signal transduction pathways that mediate the role of NS5A in hepatocarcinogenesis. HepG2 cells were transfected with a plasmid expressing HCV NS5A protein. Subsequently, cell proliferation was analysed by MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide] assay and cell counting, apoptosis was analysed by Hoechst 33342 staining, and the gene expression profile was identified by microarray and subsequently validated by RT-PCR (reverse transcription-PCR). The protein levels of survivin, p53, NOS2A (nitric oxide synthase 2A), cyclin D1 and NF-κB (nuclear factor κB) were monitored by Western blotting. Our results showed that transfection of HCV NS5A expression plasmid significantly down-regulated the expression of nine genes and up-regulated the expression of ten genes among the 104 genes detectable by the microarray associated with signalling transduction. The increased expression of survivin mRNA and protein, down-regulated p53 protein levels and increased NOS2A, cyclin D1 and NF-κB protein levels were further identified. Our results suggested that HCV NS5A protein can enhance survivin transcription by increasing p53 degradation and stimulating NOS2A expression as well as NF-κB relocation to the nucleus. The functions of survivin in anti-apoptosis and regulation of cell division might mediate the role of NS5A in HCV-induced HCC.

No abstract