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A Network Biology Approach to Discover the Molecular Biomarker Associated with Hepatocellular Carcinoma

In recent years, high throughput technologies such as microarray platform have provided a new avenue for hepatocellular carcinoma (HCC) investigation. Traditionally, gene sets enrichment analysis of survival related genes is commonly used to reveal the underlying functional mechanisms. However, this...

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Autores principales: Zhuang, Liwei, Wu, Yun, Han, Jiwu, Ling, Xiaohua, Wang, Liguo, Zhu, Chengyan, Fu, Yili
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053081/
https://www.ncbi.nlm.nih.gov/pubmed/24949431
http://dx.doi.org/10.1155/2014/278956
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author Zhuang, Liwei
Wu, Yun
Han, Jiwu
Ling, Xiaohua
Wang, Liguo
Zhu, Chengyan
Fu, Yili
author_facet Zhuang, Liwei
Wu, Yun
Han, Jiwu
Ling, Xiaohua
Wang, Liguo
Zhu, Chengyan
Fu, Yili
author_sort Zhuang, Liwei
collection PubMed
description In recent years, high throughput technologies such as microarray platform have provided a new avenue for hepatocellular carcinoma (HCC) investigation. Traditionally, gene sets enrichment analysis of survival related genes is commonly used to reveal the underlying functional mechanisms. However, this approach usually produces too many candidate genes and cannot discover detailed signaling transduction cascades, which greatly limits their clinical application such as biomarker development. In this study, we have proposed a network biology approach to discover novel biomarkers from multidimensional omics data. This approach effectively combines clinical survival data with topological characteristics of human protein interaction networks and patients expression profiling data. It can produce novel network based biomarkers together with biological understanding of molecular mechanism. We have analyzed eighty HCC expression profiling arrays and identified that extracellular matrix and programmed cell death are the main themes related to HCC progression. Compared with traditional enrichment analysis, this approach can provide concrete and testable hypothesis on functional mechanism. Furthermore, the identified subnetworks can potentially be used as suitable targets for therapeutic intervention in HCC.
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spelling pubmed-40530812014-06-19 A Network Biology Approach to Discover the Molecular Biomarker Associated with Hepatocellular Carcinoma Zhuang, Liwei Wu, Yun Han, Jiwu Ling, Xiaohua Wang, Liguo Zhu, Chengyan Fu, Yili Biomed Res Int Research Article In recent years, high throughput technologies such as microarray platform have provided a new avenue for hepatocellular carcinoma (HCC) investigation. Traditionally, gene sets enrichment analysis of survival related genes is commonly used to reveal the underlying functional mechanisms. However, this approach usually produces too many candidate genes and cannot discover detailed signaling transduction cascades, which greatly limits their clinical application such as biomarker development. In this study, we have proposed a network biology approach to discover novel biomarkers from multidimensional omics data. This approach effectively combines clinical survival data with topological characteristics of human protein interaction networks and patients expression profiling data. It can produce novel network based biomarkers together with biological understanding of molecular mechanism. We have analyzed eighty HCC expression profiling arrays and identified that extracellular matrix and programmed cell death are the main themes related to HCC progression. Compared with traditional enrichment analysis, this approach can provide concrete and testable hypothesis on functional mechanism. Furthermore, the identified subnetworks can potentially be used as suitable targets for therapeutic intervention in HCC. Hindawi Publishing Corporation 2014 2014-05-14 /pmc/articles/PMC4053081/ /pubmed/24949431 http://dx.doi.org/10.1155/2014/278956 Text en Copyright © 2014 Liwei Zhuang et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Zhuang, Liwei
Wu, Yun
Han, Jiwu
Ling, Xiaohua
Wang, Liguo
Zhu, Chengyan
Fu, Yili
A Network Biology Approach to Discover the Molecular Biomarker Associated with Hepatocellular Carcinoma
title A Network Biology Approach to Discover the Molecular Biomarker Associated with Hepatocellular Carcinoma
title_full A Network Biology Approach to Discover the Molecular Biomarker Associated with Hepatocellular Carcinoma
title_fullStr A Network Biology Approach to Discover the Molecular Biomarker Associated with Hepatocellular Carcinoma
title_full_unstemmed A Network Biology Approach to Discover the Molecular Biomarker Associated with Hepatocellular Carcinoma
title_short A Network Biology Approach to Discover the Molecular Biomarker Associated with Hepatocellular Carcinoma
title_sort network biology approach to discover the molecular biomarker associated with hepatocellular carcinoma
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4053081/
https://www.ncbi.nlm.nih.gov/pubmed/24949431
http://dx.doi.org/10.1155/2014/278956
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