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Open Access Methodology article

Detection of the inferred interaction network in hepatocellular carcinoma from EHCO (Encyclopedia of Hepatocellular Carcinoma genes Online)

Chun-Nan Hsu1, Jin-Mei Lai2, Chia-Hung Liu1, Huei-Hun Tseng3, Chih-Yun Lin4, Kuan-Ting Lin1, Hsu-Hua Yeh3, Ting-Yi Sung1, Wen-Lian Hsu1, Li-Jen Su4, Sheng-An Lee45, Chang-Han Chen34, Gen-Cher Lee15, DT Lee15, Yow-Ling Shiue6, Chang-Wei Yeh7, Chao-Hui Chang4, Cheng-Yan Kao5 and Chi-Ying F Huang34589*

Author Affiliations

1 Institute of Information Science, Academia Sinica, Taipei 115, Taiwan, R. O. C

2 Department of Life Science, Fu-Jen Catholic University, Taipei Hsien 242, Taiwan, R. O. C

3 Division of Molecular and Genomic Medicine, National Health Research Institutes, Miaoli County 350, Taiwan, R. O. C

4 Institute of Cancer Research, National Health Research Institutes, Taipei 114, Taiwan, R. O. C

5 Department of Computer Science and Information Engineering, National Taiwan University, Taipei 106, Taiwan, R. O. C

6 Institute of Biomedical Science, National Sun Yat-Sen University, Kaohsiung 804, Taiwan, R. O. C

7 National Center for High-performance Computing, Hsinchu 300, Taiwan, R. O. C

8 Institute of Bio-Pharmaceutical Sciences, National Yang-Ming University, Taipei 112, Taiwan, R. O. C

9 Institute of Biotechnology in Medicine, National Yang-Ming University, Taipei 112, Taiwan, R. O. C

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BMC Bioinformatics 2007, 8:66  doi:10.1186/1471-2105-8-66

Published: 27 February 2007



The significant advances in microarray and proteomics analyses have resulted in an exponential increase in potential new targets and have promised to shed light on the identification of disease markers and cellular pathways. We aim to collect and decipher the HCC-related genes at the systems level.


Here, we build an integrative platform, the

ncyclopedia of
arcinoma genes
nline, dubbed EHCO webcite, to systematically collect, organize and compare the pileup of unsorted HCC-related studies by using natural language processing and softbots. Among the eight gene set collections, ranging across PubMed, SAGE, microarray, and proteomics data, there are 2,906 genes in total; however, more than 77% genes are only included once, suggesting that tremendous efforts need to be exerted to characterize the relationship between HCC and these genes. Of these HCC inventories, protein binding represents the largest proportion (~25%) from Gene Ontology analysis. In fact, many differentially expressed gene sets in EHCO could form interaction networks (e.g. HBV-associated HCC network) by using available human protein-protein interaction datasets. To further highlight the potential new targets in the inferred network from EHCO, we combine comparative genomics and interactomics approaches to analyze 120 evolutionary conserved and overexpressed genes in HCC. 47 out of 120 queries can form a highly interactive network with 18 queries serving as hubs.


This architectural map may represent the first step toward the attempt to decipher the hepatocarcinogenesis at the systems level. Targeting hubs and/or disruption of the network formation might reveal novel strategy for HCC treatment.