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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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

Aparna Rai¹, Priodyuti Pradhan², Jyothi Nagraj³

¹Centre for Biosciences and Biomedical Engineering, Indian Institute of Technology Indore, Simrol, Indore, Madhya Pradesh 453552, India.

Scientific Reports

|February 4, 2017

Summary

Network analysis of seven cancers reveals common "sensor" proteins driving tumorigenesis. This finding offers insights into developing single-drug therapies for multiple cancers and personalized medicine approaches.

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Area of Science:

Computational biology
Systems biology
Cancer research

Background:

Cancer is a complex disease with diverse cellular and signaling mechanisms.
Understanding proteomic alterations in cancer is crucial for therapeutic development.

Purpose of the Study:

To analyze proteomic data from seven cancer types using network and spectral graph theory.
To identify common proteins and their roles in cancer development across different malignancies.

Main Methods:

Applied network theory and spectral graph theory to proteomic data from breast, oral, ovarian, cervical, lung, colon, and prostate cancers.
Utilized multilayer analysis to examine protein-protein interaction networks in normal and cancerous tissues.
Identified common proteins across all cancer networks, termed 'sensor' proteins.

Main Results:

Protein-protein interaction networks of normal and cancerous tissues share similar structural and spectral properties.
Unsystematic changes in network properties highlight cancer-specific interaction differences and complexity.
Identified 'sensor' proteins crucial for tumorigenesis, with significant positions across network layers.
miRNA targeting of sensor proteins suggests a role in cancer development.

Conclusions:

A novel network-based approach provides fundamental insights into cancer complexity.
Identified 'sensor' proteins represent potential therapeutic targets for multiple cancers.
Findings support the development of single-drug therapies for diverse cancers and personalized medicine strategies.