Differential combinatorial regulatory network analysis related to venous metastasis of hepatocellular carcinoma

BMC Genomics

Research

Huliang Jia¹  Lunxiu Qin¹  Qiongzhu Dong¹  Weilan Yuan²  Lingyao Zeng³  Fei He⁴  Lu Xie⁴  Jian Yu⁴  Tao Huang⁵  Yixue Li⁵ 

[1] Liver Cancer Institute and Zhongshan Hospital, Institutes of Biomedical Science, Fudan University, 200032, Shanghai, P.R.China;School of Life Science and Technology, Tongji University, 200092, Shanghai, P.R.China;School of Life Science and Technology, Tongji University, 200092, Shanghai, P.R.China;Shanghai Center for Bioinformation Technology, 200235, Shanghai, P.R.China;Shanghai Center for Bioinformation Technology, 200235, Shanghai, P.R.China;Shanghai Center for Bioinformation Technology, 200235, Shanghai, P.R.China;Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 200031, Shanghai, P.R.China;

关键词: Matthew Correlation Coefficient;    Edge Betweenness;    Network Inference Algorithm;    Construct Gene Regulatory Network;    Venous Metastasis;   

DOI  :  10.1186/1471-2164-13-S8-S14

来源: Springer

【 摘 要 】

BackgroundHepatocellular carcinoma (HCC) is one of the most fatal cancers in the world, and metastasis is a significant cause to the high mortality in patients with HCC. However, the molecular mechanism behind HCC metastasis is not fully understood. Study of regulatory networks may help investigate HCC metastasis in the way of systems biology profiling.MethodsBy utilizing both sequence information and parallel microRNA(miRNA) and mRNA expression data on the same cohort of HBV related HCC patients without or with venous metastasis, we constructed combinatorial regulatory networks of non-metastatic and metastatic HCC which contain transcription factor(TF) regulation and miRNA regulation. Differential regulation patterns, classifying marker modules, and key regulatory miRNAs were analyzed by comparing non-metastatic and metastatic networks.ResultsGlobally TFs accounted for the main part of regulation while miRNAs for the minor part of regulation. However miRNAs displayed a more active role in the metastatic network than in the non-metastatic one. Seventeen differential regulatory modules discriminative of the metastatic status were identified as cumulative-module classifier, which could also distinguish survival time. MiR-16, miR-30a, Let-7e and miR-204 were identified as key miRNA regulators contributed to HCC metastasis.ConclusionIn this work we demonstrated an integrative approach to conduct differential combinatorial regulatory network analysis in the specific context venous metastasis of HBV-HCC. Our results proposed possible transcriptional regulatory patterns underlying the different metastatic subgroups of HCC. The workflow in this study can be applied in similar context of cancer research and could also be extended to other clinical topics.

【 授权许可】

Unknown   
© Zeng et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.