TY - JOUR
T1 - Predictive model for inflammation grades of chronic hepatitis B
T2 - Large-scale analysis of clinical parameters and gene expressions
AU - Zhou, Weichen
AU - Ma, Yanyun
AU - Zhang, Jun
AU - Hu, Jingyi
AU - Zhang, Menghan
AU - Wang, Yi
AU - Li, Yi
AU - Wu, Lijun
AU - Pan, Yida
AU - Zhang, Yitong
AU - Zhang, Xiaonan
AU - Zhang, Xinxin
AU - Zhang, Zhanqing
AU - Zhang, Jiming
AU - Li, Hai
AU - Lu, Lungen
AU - Jin, Li
AU - Wang, Jiucun
AU - Yuan, Zhenghong
AU - Liu, Jie
N1 - Funding Information:
Funding information This work was supported by grants from the National Natural Science Foundation of China (31521003, 91129702 and 81125001), the Ministry of Science and Technology of China (2006AA02A411), the Major National Science and Technology Program of China (2008ZX10002-002) and the 111 Project (B13016) from Ministry of Education (MOE). Computational support was provided by the High-End Computing Center located at Fudan University. We thank Weilin Pu, Kelin Xu, Hua Dong, Chao Chen, Qianqian Peng, Feng Qian and Catherine Ketcham for their critical suggestions.
Funding Information:
This work was supported by grants from the National Natural Science Foundation of China (31521003, 91129702 and 81125001), the Ministry of Science and Technology of China (2006AA02A411), the Major National Science and Technology Program of China (2008ZX10002-002) and the 111 Project (B13016) from Ministry of Education (MOE). Computational support was provided by the High-End Computing Center located at Fudan University. Handling Editor: Mario Mondelli
Publisher Copyright:
© 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2017/11
Y1 - 2017/11
N2 - 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.
AB - 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.
KW - clinical predictive model
KW - gene expressions
KW - HBV infection
KW - inflammation grades
UR - http://www.scopus.com/inward/record.url?scp=85018582240&partnerID=8YFLogxK
U2 - 10.1111/liv.13427
DO - 10.1111/liv.13427
M3 - Article
C2 - 28328162
AN - SCOPUS:85018582240
VL - 37
SP - 1632
EP - 1641
JO - Liver
JF - Liver
SN - 1478-3223
IS - 11
ER -