# Understanding cancer complexome using networks, spectral graph theory and multilayer framework

## TL;DR

This study employs a combined network, spectral graph theory, and multilayer framework to analyze proteomic data across seven cancers, revealing key proteins and potential therapeutic targets.

## Contribution

It introduces a novel multilayer network approach integrating spectral graph theory to analyze cancer proteomes and identify critical proteins involved in tumorigenesis.

## Key findings

- Cancer networks show similar overall structural properties.
- Few proteins act as sensors across all layers and are involved in cancer causation.
- Potential miRNA interactions suggest roles in tumor development.

## Abstract

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.