  An endogenous molecular-cellular network for both normal and abnormal
functions is assumed to exist. This endogenous network forms a nonlinear
stochastic dynamical system, with many stable attractors in its functional
landscape. Normal or abnormal robust states can be decided by this network in a
manner similar to the neural network. In this context cancer is hypothesized as
one of its robust intrinsic states. This hypothesis implies that a nonlinear
stochastic mathematical cancer model is constructible based on available
experimental data and its quantitative prediction is directly testable. Within
such model the genesis and progression of cancer may be viewed as stochastic
transitions between different attractors. Thus it further suggests that
progressions are not arbitrary. Other important issues on cancer, such as
genetic vs epigenetics, double-edge effect, dormancy, are discussed in the
light of present hypothesis. A different set of strategies for cancer
prevention, cure, and care, is therefore suggested.
