Predictive model for inflammation grades of chronic hepatitis B: Large-scale analysis of clinical parameters and gene expressions

Background: Liver biopsy is the gold standard to assess pathological features (eg inflammation grades) for hepatitis B virus-infected patients although it is invasive and traumatic; meanwhile, several gene profiles of chronic hepatitis B (CHB) have been separately described in relatively small hepatitis B virus (HBV)-infected samples. We aimed to analyse correlations among inflammation grades, gene expressions and clinical parameters (serum alanine amino transaminase, aspartate amino transaminase and HBV-DNA) in large-scale CHB samples and to predict inflammation grades by using clinical parameters and/or gene expressions. Methods: We analysed gene expressions with three clinical parameters in 122 CHB samples by an improved regression model. Principal component analysis and machine-learning methods including Random Forest, K-nearest neighbour and support vector machine were used for analysis and further diagnosis models. Six normal samples were conducted to validate the predictive model. Results: Significant genes related to clinical parameters were found enriching in the immune system, interferon-stimulated, regulation of cytokine production, anti-apoptosis, and etc. A panel of these genes with clinical parameters can effectively predict binary classifications of inflammation grade (area under the ROC curve [AUC]: 0.88, 95% confidence interval [CI]: 0.77-0.93), validated by normal samples. A panel with only clinical parameters was also valuable (AUC: 0.78, 95% CI: 0.65-0.86), indicating that liquid biopsy method for detecting the pathology of CHB is possible. Conclusions: This is the first study to systematically elucidate the relationships among gene expressions, clinical parameters and pathological inflammation grades in CHB, and to build models predicting inflammation grades by gene expressions and/or clinical parameters as well.