Description
Mounting evidence points to a link between a cancer possessing stem-like properties and a worse prognosis. To understand the biology, a common approach is to integrate network biology with signal processing mechanics. That said, even with the right tools, predicting the risk for a highly susceptible target using only a handful of gene signatures remains very difficult. By compiling the expression profiles of a panel of tumor stem-like cells (TSLCs) originating in different tissues, comparing these to their parental tumor cells (PTCs) and the human embryonic stem cells (hESCs), and integrating network analysis with signaling mechanics, we propose that network topologically-weighted signaling processing measurements under tissue-specific conditions can provide scalable and predicable target identification.