Data-driven universal insights into tumorigenesis via hallmark networks.

Cancers are complex diseases characterized by dynamic perturbations of regulatory networks across multiple hierarchical levels, which cannot be fully captured by alterations in a small number of genes. To this end, based on the concept of Hallmarks of Cancer, a whole genomic data-driven approach is proposed to capture the dynamic variation from normal to cancerous cells. This framework focuses on the characteristic functional modules of cancer via hallmarks of cancer by constructing a coarse-grained gene regulatory network of hallmarks. Through this framework, with stochastic differential equations, macroscopic dynamic changes in tumorigenesis are simulated and further explored. The analysis results reveal that network topology undergoes significant reconfiguration before shifts in hallmark levels, serving as an early indicator of malignancy. A pan-cancer examination across 15 cancer types uncovers universal patterns, for example, the "Tissue Invasion and Metastasis" hallmark exhibits the most significant difference between normal and cancer states, while "Reprogramming Energy Metabolism" shows the least pronounced differences. These findings reinforce the systemic nature of cancer evolution, highlighting the potential of network-based systems biology methods for understanding critical transitions in tumorigenesis.