Cancer signaling networks are complex, involving gene regulation, signaling, and cell metabolism. Alterations in these networks caused by different mutations can lead to malignancy. We aim to evaluate these networks' computational models that allow us to understand their complex behavior better. This study aims to validate the correlation between cancer signaling pathways' complexity (clustering coefficient) and cancer epidemiological data sets, including cancer incidence, death rate, and lifetime risk. These results support the hypothesis that network complexity directly indicates cancer risk. Understanding the differential behavior of regulatory networks during health, disease, and in response to drugs is crucial for enhancing drug development efforts, identifying new targets, delineating off-target effects, predicting disease, developing combinatorial drug regimens, and developing personalized treatments targeted at the molecular level.

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