Cancer-signalling networks are typically complex which involves gene regulation, signalling, cell metabolism, and the alterations in its dynamics caused by the several different types of mutations leading to malignancy. Computational model of networks make possible to understand the complex behaviour of cancer-signalling network. Correlation between complexity (clustering coefficient) of cancer-signalling network pathway and Cancer Epidemiological data sets (Cancer incidence, Death rate and lifetime risk of cancer) has been validated. Results of study support the initial assumption, that the complexity of network matrices is a direct indicator of cancer threat. Understanding the differential behaviour of regulatory networks during health, disease and in response to drugs play a crucial role to enhance drug development efforts, new target identification, delineation of off-target effects, methods of disease prediction, combinatorial drug regimens and also in development of molecularly targeted personalized treatment.