Department of Digestive Diseases of Huashan Hospital, Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai, China.
State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
Liver Int. 2017 Nov;37(11):1632-1641. doi: 10.1111/liv.13427. Epub 2017 Apr 19.
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.
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.
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.
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.
肝活检是评估乙型肝炎病毒（HBV）感染患者病理特征（如炎症分级）的金标准，尽管它具有侵袭性和创伤性；同时，已有多项慢性乙型肝炎（CHB）的基因谱在相对较小的 HBV 感染样本中分别进行了描述。我们旨在分析大样本 CHB 中炎症分级、基因表达与临床参数（血清丙氨酸氨基转移酶、天冬氨酸氨基转移酶和 HBV-DNA）之间的相关性，并使用临床参数和/或基因表达来预测炎症分级。
我们通过改进的回归模型，分析了 122 例 CHB 样本中三个临床参数的基因表达。采用主成分分析和机器学习方法，包括随机森林、K-最近邻和支持向量机，进行分析和进一步的诊断模型。对 6 个正常样本进行了验证预测模型。
与临床参数相关的显著基因富集在免疫系统、干扰素刺激、细胞因子产生调控、抗凋亡等方面。这些基因与临床参数的组合可以有效地预测炎症分级的二分法（ROC 曲线下面积 [AUC]：0.88，95%置信区间 [CI]：0.77-0.93），并通过正常样本得到验证。仅使用临床参数的组合也具有价值（AUC：0.78，95%CI：0.65-0.86），表明液体活检方法检测 CHB 的病理学是可行的。
这是第一项系统阐明 CHB 中基因表达、临床参数与病理炎症分级之间关系的研究，并建立了通过基因表达和/或临床参数预测炎症分级的模型。
