Viral quasispecies quantitative analysis: a novel approach for appraising the immune tolerant phase of chronic hepatitis B virus infection

Few non-invasive models were established for precisely identifying the immune tolerant (IT) phase from chronic hepatitis B (CHB). This study aimed to develop a novel approach that combined next-generation sequencing (NGS) and machine learning algorithms using our recently published viral quasispecie...

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Bibliographic Details
Published in:Emerging microbes & infections 2021-01, Vol.10 (1), p.842-851
Main Authors: Wang, Mingjie, Chen, Li, Dong, MinHui, Li, Jing, Zhu, Beidi, Yang, Zhitao, Gong, Qiming, Han, Yue, Yu, Demin, Zhang, Donghua, Zoulim, Fabien, Zhang, Jiming, Zhang, Xinxin
Format: Article
Language:English
Subjects:
Adult
Chronic hepatitis B
clinical pathology
Cluster Analysis
Decision Support Systems, Clinical
decision support techniques
Deep Learning
Female
Hepatitis
Hepatitis B Surface Antigens - blood
Hepatitis B virus - genetics
Hepatitis B virus - immunology
Hepatitis B, Chronic - blood
Hepatitis B, Chronic - immunology
Hepatitis B, Chronic - virology
Human health and pathology
Humans
Hépatology and Gastroenterology
Immune Tolerance
Life Sciences
machine learning
Male
Middle Aged
natural history
Principal Component Analysis
Quasispecies
Retrospective Studies
Sequence Analysis, DNA - methods
Support Vector Machine
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Summary:Few non-invasive models were established for precisely identifying the immune tolerant (IT) phase from chronic hepatitis B (CHB). This study aimed to develop a novel approach that combined next-generation sequencing (NGS) and machine learning algorithms using our recently published viral quasispecies (QS) analysis package. 290 HBeAg positive patients from whom liver biopsies were taken were enrolled and divided into a training group (n = 148) and a validation group (n = 142). HBV DNA was extracted and QS sequences were obtained by NGS. Hierarchical clustering analysis (HCA) and principal component analysis (PCA) based on viral operational taxonomic units (OTUs) were performed to explore the correlations among QS and clinical phenotypes. Three machine learning algorithms, including K-nearest neighbour, support vector machine, and random forest algorithm, were used to construct diagnostic models for IT phase classification. Based on histopathology, 90 IT patients and 200 CHB patients were diagnosed. HBsAg titres for IT patients were higher than those of CHB patients (p 
ISSN:2222-1751
2222-1751
DOI:10.1080/22221751.2021.1919033