Abstract
Specific molecular signaling networks underlie different cancer types and quantitative analyses on those cancer networks can provide useful information about cancer treatments. Their structural metrics can reveal survivability of cancer patients and be used to identify biomarker genes for early cancer detection. In this study, we devised a novel structural metric called hierarchical closeness (HC) entropy and found that it was negatively correlated with 5-year survival rates. We also made an interesting observation that a network of higher HC entropy was likely to be more robust against mutations. This finding suggested that cancers of high HC entropy tend to be incurable because their signaling networks are robust to perturbations caused by treatment. We also proposed a novel core identification method based on the reachability factor in the HC measure. The cores were permitted to decompose such that the negative relationship between HC entropy and cancer survival rate was consistently conserved in every core level. Interestingly, we observed that many promising biomarker genes for early cancer detection reside in the innermost core of a signaling network. Taken together, the proposed analyses of the hierarchical structure of cancer signaling networks may be useful in developing future novel cancer treatments.
Highlights
Cancer is a leading cause of disease worldwide with more than 11 million people diagnosed every year
hierarchical closeness (HC) entropy showed a significant negative relationship (Fig 2B; Spearman’s rank correlation coefficient Rs = −0.591, P = 0.022). This comparison suggests that HC entropy is a more accurate measure of 5-year cancer survival rates
There are different subtypes in Melanoma cancer type, which have been characterized over the years: BRAF mutants [71, 72], KRAS mutants [73], RAC1 mutants [74], EGF mutations [75], and other gene mutants
Summary
Cancer is a leading cause of disease worldwide with more than 11 million people diagnosed every year. It is estimated that by 2030 there will be approximately 26 million new cancer cases and 17 million cancer deaths worldwide per year [1]. Cancer is a genetic disease where one or more mutated genes result in abnormal cell proliferation. Diagnosis and personalized therapy often rely on insights from relevant molecular signaling pathways as well as cancerrelated genes. In terms of network dynamics, a signaling network converges on a stable equilibrium state (an ordinary attractor), which corresponds to a normal cellular state, but a genetic mutation may attract a cell to a malignant phenotypic state (a cancer attractor), which eventually results in cancer development [2].
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
