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Transcriptomic Heterogeneity in Cancer as a Consequence of Dysregulation of the Gene-Gene Interaction Network.

van Wieringen WN, van der Vaart AW - Bull. Math. Biol. (2015)

Bottom Line: Dysregulation of the regulatory network results in less control of transcript levels in the cell.Hence, dysregulation is reflected in the heterogeneity of the transcriptome: the more dysregulated the pathway, the more the transcriptomic heterogeneity.These mechanisms are statistically motivated, explored in silico, and their plausibility to occur in vivo illustrated by means of oncogenomics data of breast cancer studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Biostatistics, VU University Medical Center, P. O. Box 7057, 1007 MB, Amsterdam, The Netherlands. w.vanwieringen@vumc.nl.

ABSTRACT
Many pathways are dysregulated in cancer. Dysregulation of the regulatory network results in less control of transcript levels in the cell. Hence, dysregulation is reflected in the heterogeneity of the transcriptome: the more dysregulated the pathway, the more the transcriptomic heterogeneity. We identify four scenarios for a transcriptomic heterogeneity increase (i.e., pathway dysregulation) in cancer: (1) activation of a molecular switch, (2) a structural change in a regulator, (3) a temporal change in a regulator, and (4) weakening of gene-gene interactions. These mechanisms are statistically motivated, explored in silico, and their plausibility to occur in vivo illustrated by means of oncogenomics data of breast cancer studies.

No MeSH data available.


Related in: MedlinePlus

Network changes over time. The fully connected graph on the left is a caricature of a regulatory network. The width of the edges is proportional to their hypothesized strengths. Over time, as the disease progresses, interactions between nodes weaken, which is reflected by the decreased width of some of the edges. Eventually, some of these interactions get lost (symbolized by edges that have disappeared), and the graph may even become disconnected
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Fig6: Network changes over time. The fully connected graph on the left is a caricature of a regulatory network. The width of the edges is proportional to their hypothesized strengths. Over time, as the disease progresses, interactions between nodes weaken, which is reflected by the decreased width of some of the edges. Eventually, some of these interactions get lost (symbolized by edges that have disappeared), and the graph may even become disconnected

Mentions: Changes in the architecture of a regulatory network may also affect the cell’s entropy. In particular, as we show here, the weakening of an edge may lead to a surge in the network’s entropy. An extreme case of this phenomenon is the removal of an edge, which indeed may further increase the entropy. Figure 6 illustrates the three cases: the original network (representing the normal, healthy state), the same network with some edges weakened (an early disease state), and, finally, a disconnected network (the late disease state).Fig. 6


Transcriptomic Heterogeneity in Cancer as a Consequence of Dysregulation of the Gene-Gene Interaction Network.

van Wieringen WN, van der Vaart AW - Bull. Math. Biol. (2015)

Network changes over time. The fully connected graph on the left is a caricature of a regulatory network. The width of the edges is proportional to their hypothesized strengths. Over time, as the disease progresses, interactions between nodes weaken, which is reflected by the decreased width of some of the edges. Eventually, some of these interactions get lost (symbolized by edges that have disappeared), and the graph may even become disconnected
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4644214&req=5

Fig6: Network changes over time. The fully connected graph on the left is a caricature of a regulatory network. The width of the edges is proportional to their hypothesized strengths. Over time, as the disease progresses, interactions between nodes weaken, which is reflected by the decreased width of some of the edges. Eventually, some of these interactions get lost (symbolized by edges that have disappeared), and the graph may even become disconnected
Mentions: Changes in the architecture of a regulatory network may also affect the cell’s entropy. In particular, as we show here, the weakening of an edge may lead to a surge in the network’s entropy. An extreme case of this phenomenon is the removal of an edge, which indeed may further increase the entropy. Figure 6 illustrates the three cases: the original network (representing the normal, healthy state), the same network with some edges weakened (an early disease state), and, finally, a disconnected network (the late disease state).Fig. 6

Bottom Line: Dysregulation of the regulatory network results in less control of transcript levels in the cell.Hence, dysregulation is reflected in the heterogeneity of the transcriptome: the more dysregulated the pathway, the more the transcriptomic heterogeneity.These mechanisms are statistically motivated, explored in silico, and their plausibility to occur in vivo illustrated by means of oncogenomics data of breast cancer studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Biostatistics, VU University Medical Center, P. O. Box 7057, 1007 MB, Amsterdam, The Netherlands. w.vanwieringen@vumc.nl.

ABSTRACT
Many pathways are dysregulated in cancer. Dysregulation of the regulatory network results in less control of transcript levels in the cell. Hence, dysregulation is reflected in the heterogeneity of the transcriptome: the more dysregulated the pathway, the more the transcriptomic heterogeneity. We identify four scenarios for a transcriptomic heterogeneity increase (i.e., pathway dysregulation) in cancer: (1) activation of a molecular switch, (2) a structural change in a regulator, (3) a temporal change in a regulator, and (4) weakening of gene-gene interactions. These mechanisms are statistically motivated, explored in silico, and their plausibility to occur in vivo illustrated by means of oncogenomics data of breast cancer studies.

No MeSH data available.


Related in: MedlinePlus