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© 2020 Teng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Hepatocellular carcinoma (HCC) is among the leading causes of cancer-related death worldwide. Patients with hepatitis B virus (HBV) pre-S mutants in liver tissues or blood have been regarded as a high-risk population for HCC development and recurrence. Detection of pre-S mutants in clinical specimens is thus important for early diagnosis and prognosis of HCC to improve patient survival. Recently, we have developed a next-generation sequencing (NGS)-based platform that can quantitatively detect pre-S mutants in patient plasma with superior sensitivity and accuracy. In this study, we compared the pre-S genotyping results from plasma by the NGS-based analysis with those from liver tissues by the immunohistochemistry (IHC)-based analysis in 30 HBV-related HCC patients. We demonstrated that the detection rate of pre-S mutants was significantly higher by NGS- than by IHC-based analysis. There was a moderate to good agreement between both analyses in detection of pre-S mutants. Compared with the IHC, the NGS-based detection of pre-S mutants in patient plasma could determine the patterns of pre-S mutants in liver tissues more efficiently in a noninvasive manner. Our data suggest that the NGS-based platform may represent a promising approach for detection of pre-S mutants as biomarkers of HBV-related HCC in clinical practice.

Details

Title
Detection of hepatitis B virus pre-S mutants in plasma by a next-generation sequencing-based platform determines their patterns in liver tissues
Author
Chiao-Fang Teng; Hung-Wen, Tsai; Tsai-Chung, Li; Wang, Ting; Wang, John; Shyu, Woei-Cherng; Wu, Han-Chieh; Ih-Jen Su; Long-Bin Jeng
First page
e0234773
Section
Research Article
Publication year
2020
Publication date
Jun 2020
Publisher
Public Library of Science
e-ISSN
19326203
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2415000529
Copyright
© 2020 Teng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.