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Randomness and preserved patterns in cancer network - PubMed

  • ️Wed Jan 01 2014

Randomness and preserved patterns in cancer network

Aparna Rai et al. Sci Rep. 2014.

Abstract

Breast cancer has been reported to account for the maximum cases among all female cancers till date. In order to gain a deeper insight into the complexities of the disease, we analyze the breast cancer network and its normal counterpart at the proteomic level. While the short range correlations in the eigenvalues exhibiting universality provide an evidence towards the importance of random connections in the underlying networks, the long range correlations along with the localization properties reveal insightful structural patterns involving functionally important proteins. The analysis provides a benchmark for designing drugs which can target a subgraph instead of individual proteins.

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Conflict of interest statement

The authors declare no competing financial interests.

Figures

Figure 1
Figure 1. Degree distribution and degree-CC correlations for the normal and disease networks.

Left panel of the normal network show that the degree distribution follows power law and the degree clustering coefficient correlation shows are negatively correlated. The right panel gives us the same results for the disease networks.

Figure 2
Figure 2. Eigenvalue distribution of both normal and disease networks.

The plots depict triangular distribution for both the networks with a high degeneracy at zero.

Figure 3
Figure 3. Spacing distribution.

The ratio of eigenvalues spacing follow GOE statistics for both the networks. The bars represent data points and solid line represents Eq.4 with parameters of GOE statistics.

Figure 4
Figure 4. Long-range Correlations (Δ3 statistics) for the normal and the disease network.

Circles denote data points for the normal and the disease networks whereas the solid line is the Δ3 statistics for the GOE.

Figure 5
Figure 5. Eigenvector Localization both normal and disease network.

They clearly reflect three regions (i) degenerate part in the middle, (ii) a large non-degenerate part which follow GOE statistics of RMT and non-degenerate part at both the end and near to the zero eigenvalues which deviate from RMT.

Figure 6
Figure 6. Local structure of top contributing nodes.

(Left panel) The local structure of all TCNs in the disease network. (Right panel) The local structure for the same proteins in the normal network. Yellow represents TCNs and pink represent their first neighbor.

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References

    1. Venter J. C. et al. The Sequence of the Human Genome. Science 16, 1304–1351 (2001). - PubMed
    1. Ferlay J., Parkin D. M. & Fouchera E. S. Estimates of cancer incidence and mortality in Europe in 2008. EJC. 46, 765–781 (2010). - PubMed
    1. Kiberstis P. & Roberts L. A Race Still Unfinished. Science 343, 1451–1470 (2014). - PubMed
    1. Hanahan D. & Weinberg R. A. The Hallmarks of Cancer. Cell 100, 57–70 (2000). - PubMed
    1. Petricoin E. F. et al. Mapping Molecular Networks Using Proteomics: A Vision for Patient-Tailored Combination Therapy. J. Clin. Oncol. 23, 3614–3621 (2005). - PubMed

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