  Cancer complexome comprises a heterogeneous and multifactorial milieu that
varies in cytology, physiology, signaling mechanisms and response to therapy.
The combined framework of network theory and spectral graph theory along with
the multilayer anal- ysis provides a comprehensive approach to analyze the
proteomic data of seven different cancers, namely, breast, oral, ovarian,
cervical, lung, colon and prostate. Our analysis demonstrates that the
protein-protein interaction networks of the normal and the cancerous tissues
associated with the seven cancers have overall similar structural and spectral
properties. However, few of these properties implicate unsystematic changes
from the normal to the disease networks depicting difference in the
interactions and highlighting changes in the complexity of different cancers.
Importantly, analysis of common proteins of all the cancer networks reveals few
proteins namely the sensors, which not only occupy significant position in all
the layers but also have direct involvement in causing cancer. The prediction
and analysis of miRNAs targeting these sensor proteins hint towards the
possible role of these proteins in tumorigenesis. This novel approach helps in
understanding cancer at the fundamental level and provides a clue to develop
promising and nascent concept of single drug therapy for multiple diseases as
well as personalized medicine.
