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Dynamic edge-based biomarker non-invasively predicts hepatocellular carcinoma with hepatitis B virus infection for individual patients based on blood testing
Hepatitis B virus (HBV)-induced hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths in Asia and Africa. Developing effective and non-invasive biomarkers of HCC for individual patients remains an urgent task for early diagnosis and convenient monitoring. Analyzing the transcripto...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6788726/ https://www.ncbi.nlm.nih.gov/pubmed/30925583 http://dx.doi.org/10.1093/jmcb/mjz025 |
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author | Lu, Yiyu Fang, Zhaoyuan Li, Meiyi Chen, Qian Zeng, Tao Lu, Lina Chen, Qilong Zhang, Hui Zhou, Qianmei Sun, Yan Xue, Xuefeng Hu, Yiyang Chen, Luonan Su, Shibing |
author_facet | Lu, Yiyu Fang, Zhaoyuan Li, Meiyi Chen, Qian Zeng, Tao Lu, Lina Chen, Qilong Zhang, Hui Zhou, Qianmei Sun, Yan Xue, Xuefeng Hu, Yiyang Chen, Luonan Su, Shibing |
author_sort | Lu, Yiyu |
collection | PubMed |
description | Hepatitis B virus (HBV)-induced hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths in Asia and Africa. Developing effective and non-invasive biomarkers of HCC for individual patients remains an urgent task for early diagnosis and convenient monitoring. Analyzing the transcriptomic profiles of peripheral blood mononuclear cells from both healthy donors and patients with chronic HBV infection in different states (i.e. HBV carrier, chronic hepatitis B, cirrhosis, and HCC), we identified a set of 19 candidate genes according to our algorithm of dynamic network biomarkers. These genes can both characterize different stages during HCC progression and identify cirrhosis as the critical transition stage before carcinogenesis. The interaction effects (i.e. co-expressions) of candidate genes were used to build an accurate prediction model: the so-called edge-based biomarker. Considering the convenience and robustness of biomarkers in clinical applications, we performed functional analysis, validated candidate genes in other independent samples of our collected cohort, and finally selected COL5A1, HLA-DQB1, MMP2, and CDK4 to build edge panel as prediction models. We demonstrated that the edge panel had great performance in both diagnosis and prognosis in terms of precision and specificity for HCC, especially for patients with alpha-fetoprotein-negative HCC. Our study not only provides a novel edge-based biomarker for non-invasive and effective diagnosis of HBV-associated HCC to each individual patient but also introduces a new way to integrate the interaction terms of individual molecules for clinical diagnosis and prognosis from the network and dynamics perspectives. |
format | Online Article Text |
id | pubmed-6788726 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-67887262019-10-18 Dynamic edge-based biomarker non-invasively predicts hepatocellular carcinoma with hepatitis B virus infection for individual patients based on blood testing Lu, Yiyu Fang, Zhaoyuan Li, Meiyi Chen, Qian Zeng, Tao Lu, Lina Chen, Qilong Zhang, Hui Zhou, Qianmei Sun, Yan Xue, Xuefeng Hu, Yiyang Chen, Luonan Su, Shibing J Mol Cell Biol Original Article Hepatitis B virus (HBV)-induced hepatocellular carcinoma (HCC) is a major cause of cancer-related deaths in Asia and Africa. Developing effective and non-invasive biomarkers of HCC for individual patients remains an urgent task for early diagnosis and convenient monitoring. Analyzing the transcriptomic profiles of peripheral blood mononuclear cells from both healthy donors and patients with chronic HBV infection in different states (i.e. HBV carrier, chronic hepatitis B, cirrhosis, and HCC), we identified a set of 19 candidate genes according to our algorithm of dynamic network biomarkers. These genes can both characterize different stages during HCC progression and identify cirrhosis as the critical transition stage before carcinogenesis. The interaction effects (i.e. co-expressions) of candidate genes were used to build an accurate prediction model: the so-called edge-based biomarker. Considering the convenience and robustness of biomarkers in clinical applications, we performed functional analysis, validated candidate genes in other independent samples of our collected cohort, and finally selected COL5A1, HLA-DQB1, MMP2, and CDK4 to build edge panel as prediction models. We demonstrated that the edge panel had great performance in both diagnosis and prognosis in terms of precision and specificity for HCC, especially for patients with alpha-fetoprotein-negative HCC. Our study not only provides a novel edge-based biomarker for non-invasive and effective diagnosis of HBV-associated HCC to each individual patient but also introduces a new way to integrate the interaction terms of individual molecules for clinical diagnosis and prognosis from the network and dynamics perspectives. Oxford University Press 2019-04-08 /pmc/articles/PMC6788726/ /pubmed/30925583 http://dx.doi.org/10.1093/jmcb/mjz025 Text en © The Author(s) 2019. Published by Oxford University Press on behalf of Journal of Molecular Cell Biology, IBCB, SIBS, CAS. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Original Article Lu, Yiyu Fang, Zhaoyuan Li, Meiyi Chen, Qian Zeng, Tao Lu, Lina Chen, Qilong Zhang, Hui Zhou, Qianmei Sun, Yan Xue, Xuefeng Hu, Yiyang Chen, Luonan Su, Shibing Dynamic edge-based biomarker non-invasively predicts hepatocellular carcinoma with hepatitis B virus infection for individual patients based on blood testing |
title | Dynamic edge-based biomarker non-invasively predicts hepatocellular carcinoma with hepatitis B virus infection for individual patients based on blood testing |
title_full | Dynamic edge-based biomarker non-invasively predicts hepatocellular carcinoma with hepatitis B virus infection for individual patients based on blood testing |
title_fullStr | Dynamic edge-based biomarker non-invasively predicts hepatocellular carcinoma with hepatitis B virus infection for individual patients based on blood testing |
title_full_unstemmed | Dynamic edge-based biomarker non-invasively predicts hepatocellular carcinoma with hepatitis B virus infection for individual patients based on blood testing |
title_short | Dynamic edge-based biomarker non-invasively predicts hepatocellular carcinoma with hepatitis B virus infection for individual patients based on blood testing |
title_sort | dynamic edge-based biomarker non-invasively predicts hepatocellular carcinoma with hepatitis b virus infection for individual patients based on blood testing |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6788726/ https://www.ncbi.nlm.nih.gov/pubmed/30925583 http://dx.doi.org/10.1093/jmcb/mjz025 |
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