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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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
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.
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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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