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Assessment of subnetwork detection methods for breast cancer.

Jiang B, Gribskov M - Cancer Inform (2014)

Bottom Line: Here, we compare the results of eight methods: simulated annealing-based jActiveModules, greedy search-based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker.While the number of genes/proteins and protein interactions detected by the eight methods vary widely, a core set of 60 genes and 50 interactions was found to be shared by the subnetworks identified by five or more of the methods.Within the core set, 12 genes were found to be known breast cancer genes.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.

ABSTRACT
Subnetwork detection is often used with differential expression analysis to identify modules or pathways associated with a disease or condition. Many computational methods are available for subnetwork analysis. Here, we compare the results of eight methods: simulated annealing-based jActiveModules, greedy search-based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker. These methods represent distinctly different computational strategies and are among the most widely used. Each of these methods was used to analyze gene expression data consisting of paired tumor and normal samples from 50 breast cancer patients. While the number of genes/proteins and protein interactions detected by the eight methods vary widely, a core set of 60 genes and 50 interactions was found to be shared by the subnetworks identified by five or more of the methods. Within the core set, 12 genes were found to be known breast cancer genes.

No MeSH data available.


Related in: MedlinePlus

Prominent subnetwork whose interactions are detected by at least five methods. Node color indicates log2 fold change of differential expression (yellow: upregulated in tumor samples; blue: downregulated in tumor samples). The 12 genes in red border are in the list of 462 known breast cancer genes. Visualized by Cytoscape 3.0 version.6
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f6-cin-suppl.6-2014-015: Prominent subnetwork whose interactions are detected by at least five methods. Node color indicates log2 fold change of differential expression (yellow: upregulated in tumor samples; blue: downregulated in tumor samples). The 12 genes in red border are in the list of 462 known breast cancer genes. Visualized by Cytoscape 3.0 version.6

Mentions: Finally, we used Cytoscape 3.021 to visualize a prominent subnetwork in which each interaction is detected by at least five methods. This subnetwork consists of 60 genes and 50 interactions (Fig. 6). Within those 60 genes, there are 12 true breast cancer genes (red border) detected by KOBAS 2.0 in the multiple databases. Notably, the breast cancer gene Nuclear Receptor Subfamily 3, Group C, Member 2 (NR3C2), a gene encoding the mineralocorticoid receptor, was the only gene detected by all the eight methods. An RNA interference (RNAi) experiment has verified that the depletion of NR3C2 increases cell death in breast.22 This evidence is consistent with Figure 6 in which NR3C2 is downregulated in breast cancer cells (log2(fold change) = −2.2). We also found that actin alpha 1 (ACTA1), one of the interactors of NR3C2, was detected by five methods and was downregulated as well. ACTA1 is a highly conserved protein responsible for cell motility and a major constituent of the contractile apparatus.23 This suggests that downregulation of ACTA1 causes increased cell motility and cancer metastasis. Similarly, inhibin, beta A (INHBA), pleiotrophin (PTN), and seven in absentia homolog family E3 (siah E3) ubiquitin protein ligase 2 (SIAH2), which were detected by seven methods, have been experimentally verified to be associated with breast cancer development. Overexpression of INHBA in mesenchymal cells increases colony formation potential of breast epithelial cells.24 PTN, a secretory cytokine, has been found to stimulate breast cancer progression through remodeling of the tumor microenvironment.25 Downregulation of SIAH2 has been found to be associated with resistance to endocrine therapy in breast cancer.26


Assessment of subnetwork detection methods for breast cancer.

Jiang B, Gribskov M - Cancer Inform (2014)

Prominent subnetwork whose interactions are detected by at least five methods. Node color indicates log2 fold change of differential expression (yellow: upregulated in tumor samples; blue: downregulated in tumor samples). The 12 genes in red border are in the list of 462 known breast cancer genes. Visualized by Cytoscape 3.0 version.6
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4256043&req=5

f6-cin-suppl.6-2014-015: Prominent subnetwork whose interactions are detected by at least five methods. Node color indicates log2 fold change of differential expression (yellow: upregulated in tumor samples; blue: downregulated in tumor samples). The 12 genes in red border are in the list of 462 known breast cancer genes. Visualized by Cytoscape 3.0 version.6
Mentions: Finally, we used Cytoscape 3.021 to visualize a prominent subnetwork in which each interaction is detected by at least five methods. This subnetwork consists of 60 genes and 50 interactions (Fig. 6). Within those 60 genes, there are 12 true breast cancer genes (red border) detected by KOBAS 2.0 in the multiple databases. Notably, the breast cancer gene Nuclear Receptor Subfamily 3, Group C, Member 2 (NR3C2), a gene encoding the mineralocorticoid receptor, was the only gene detected by all the eight methods. An RNA interference (RNAi) experiment has verified that the depletion of NR3C2 increases cell death in breast.22 This evidence is consistent with Figure 6 in which NR3C2 is downregulated in breast cancer cells (log2(fold change) = −2.2). We also found that actin alpha 1 (ACTA1), one of the interactors of NR3C2, was detected by five methods and was downregulated as well. ACTA1 is a highly conserved protein responsible for cell motility and a major constituent of the contractile apparatus.23 This suggests that downregulation of ACTA1 causes increased cell motility and cancer metastasis. Similarly, inhibin, beta A (INHBA), pleiotrophin (PTN), and seven in absentia homolog family E3 (siah E3) ubiquitin protein ligase 2 (SIAH2), which were detected by seven methods, have been experimentally verified to be associated with breast cancer development. Overexpression of INHBA in mesenchymal cells increases colony formation potential of breast epithelial cells.24 PTN, a secretory cytokine, has been found to stimulate breast cancer progression through remodeling of the tumor microenvironment.25 Downregulation of SIAH2 has been found to be associated with resistance to endocrine therapy in breast cancer.26

Bottom Line: Here, we compare the results of eight methods: simulated annealing-based jActiveModules, greedy search-based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker.While the number of genes/proteins and protein interactions detected by the eight methods vary widely, a core set of 60 genes and 50 interactions was found to be shared by the subnetworks identified by five or more of the methods.Within the core set, 12 genes were found to be known breast cancer genes.

View Article: PubMed Central - PubMed

Affiliation: Department of Biological Sciences, Purdue University, West Lafayette, IN, USA.

ABSTRACT
Subnetwork detection is often used with differential expression analysis to identify modules or pathways associated with a disease or condition. Many computational methods are available for subnetwork analysis. Here, we compare the results of eight methods: simulated annealing-based jActiveModules, greedy search-based jActiveModules, DEGAS, BioNet, NetBox, ClustEx, OptDis, and NetWalker. These methods represent distinctly different computational strategies and are among the most widely used. Each of these methods was used to analyze gene expression data consisting of paired tumor and normal samples from 50 breast cancer patients. While the number of genes/proteins and protein interactions detected by the eight methods vary widely, a core set of 60 genes and 50 interactions was found to be shared by the subnetworks identified by five or more of the methods. Within the core set, 12 genes were found to be known breast cancer genes.

No MeSH data available.


Related in: MedlinePlus