Randomness and preserved patterns in cancer network

TL;DR

This study analyzes breast cancer and normal protein networks to uncover the balance of randomness and structural patterns, offering insights for targeted drug design.

Contribution

It provides a novel proteomic network analysis revealing the interplay of randomness and structural patterns in cancer.

Findings

Short-range correlations suggest importance of random connections.

Long-range correlations reveal structural patterns involving key proteins.

Analysis offers a benchmark for subgraph-targeted drug design.

Abstract

Breast cancer has been reported to account for the maximum cases among all female cancers till date. In order to gain a deeper insight into the complexities of the disease, we analyze the breast cancer network and its normal counterpart at the proteomic level. While the short range correlations in the eigenvalues exhibiting universality provide an evidence towards the importance of random connections in the underlying networks, the long range correlations along with the localization properties reveal insightful structural patterns involving functionally important proteins. The analysis provides a benchmark for designing drugs which can target a subgraph instead of individual proteins.