Hepatocellular Carcinoma Gene/Protein Networks

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Hepatocellular carcinoma is considered the leading cause of death among people affected by hepatitis C virus-induced cirrhosis. This Demonstration integrates gene expressions from DNA microarrays with protein expressions from hepatocellular carcinoma and offers a global visualization of data currently available, suggesting an easy way to browse complex biological information and findings along the tumorigenesis development.

Contributed by: Luca Zammataro (August 2011)
Open content licensed under CC BY-NC-SA



Background and Methods

Gene expressions were obtained from DNA microarrays produced by Elisa Wurmbach and collaborators [1]. The microarray is available at: http://www.ncbi.nlm.nih.gov/geo (GSE 6764) [2]. Gene expressions are clustered in seven observations that you can browse by means of the seven control buttons: (1) cirrhosis versus control ("Cirrhosis"); (2) low-grade nodules versus control ("LG-nodule"); (3) high-grade nodules versus control ("HG-nodule"); (4) very early cancer versus control ("Very Early"); (5) early cancer versus control ("Early"); (6) advanced cancer versus control ("Advanced"); and (7) very advanced cancer versus control ("Very Advanced").

Genes were defined as regulated when characterized by Student t test -values less than 0.01 in at least one observation. Statistics were performed by means of R bioconductor statistics programming environment (http://www.r-project.org) [3].

The dataset containing 11000 gene identifiers and corresponding expression values was then intersected with another dataset from protein expressions, produced by Tadashi Kondo [4], containing 21 proteomes from liver cancer.

After the genome/proteome intersection, a list of 290 annotated molecules was obtained and then uploaded into the Ingenuity Pathways Analysis application [5]. Each identifier was mapped to its corresponding object in Ingenuity's Knowledge Base. These molecules, called network-eligible molecules, were overlaid onto a global molecular network developed from information contained in Ingenuity's Knowledge Base. Networks of network-eligible molecules were then algorithmically generated based on their connectivity. The functional analysis of a network has identified the biological functions that were most significant to the molecules in the network. Only molecule networks associated with biological functions in Ingenuity's Knowledge Base were considered for the analysis.

Right-tailed Fisher's exact test was used to calculate a -value determining the probability that each biological function assigned to that network is due to chance alone. A total list of 16 networks from the 290 regulated genes was obtained. The Ingenuity System has added other molecules to obtain consistent networks. The total list of molecules present in this Demonstration is 529.

Each molecule within the 16 networks was then annotated considering the possible cell expression, by means of Biomart resources [6].


1. After you have chosen which of the 16 networks (metabolism) you want to browse, you can visualize gene expressions, protein expressions, molecular functions, location, or biomarkers by means of the "view" control bar.

2. Gene and protein expressions are represented by two colors: red for upregulated molecules and green for downregulated molecules.

3. You can choose to represent molecules as simple disks or shapes corresponding to biological function.

4. Blurred disks/objects correspond to molecule regulations that are not statistically significant. You can also choose the disk size and the graph size.

5. You can choose to visualize text on disks, using the "text" check box. Names of molecules will be displayed on disks.

6. You can visualize directional arrows for each edge in networks using the "arrows" check box.

7. You can highlight molecules with biomarker functions by enabling the "track biomarker" check box.

8. You can change the edge distances with the "edge size" control.

9. You can choose the graph method display.

10. Search a specific molecule, first enabling the "jumping" check box, and then selecting a molecule using the "jump to a molecule" menu: the Demonstration will show you the relevant network, showing the molecule as yellow.

11. Select the fold-change threshold using the "threshold" control bar.

12. You can display molecule information as a tooltip by mousing over a disk.


[1] E. Wurmbach, Y. B. Chen, G. Khitrov, W. Zhang, S. Roayaie, M. Schwartz, I. Fiel, S. Thung, V. Mazzaferro, J. Bruix, E. Bottinger, S. Friedman, S. Waxman, and J. M. Llovet, "Genome-Wide Molecular Profiles of HCV-Induced Dysplasia and Hepatocellular Carcinoma," Hepatology, 45(4), 2007 pp. 938–947, PubMed PMID: 17393520. http://onlinelibrary.wiley.com/doi/10.1002/hep.v45:4/issuetoc.

[2] http://www.ncbi.nlm.nih.gov/geo

[3] http://www.r-project.org

[4] T. Kondo, "Cancer Proteome-Expression Database: Genome Medicine Database of Japan Proteomics," Expert Review of Proteomics, 7(1), 2010 pp. 21–27. http://www.expert-reviews.com/doi/abs/10.1586/epr.09.87?journalCode=epr.

[5] http://www.ingenuity.com

[6] http://www.biomart.org/biomart/martview

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