Large-scale prediction of key dynamic interacting proteins in multiple cancers

Area of Science:

• Oncology
• Systems Biology
• Bioinformatics

Background:

• Tracking dynamic protein-protein interactions (PPIs) in cancer and understanding their role in pathogenesis is challenging.
• A hypothesis proposes that dynamic switching between permanent and transient PPIs contributes to cancer development by altering cellular functions.

Purpose of the Study:

• To identify key cancer genes (KCGs) and key dynamic interactions (KDIs) based on a dynamic PPI hypothesis.
• To investigate the functional and network characteristics of these KDIs in relation to cancer hallmarks.

Main Methods:

• Predicted over 1400 KCGs using the PPI-express method on 18 cancer gene expression datasets.
• Screened for KDIs by analyzing KCGs and the transient/permanent nature of interactions under normal and cancer conditions.
• Analyzed network properties, including edge betweenness and module localization, for different KDI types (P2T and T2P).

Main Results:

• Identified "Cell cycle-related" and "Immune-related" as prominent functional characteristics of KCGs.
• Found that transient to permanent (T2P) KDIs exhibit significantly higher edge betweenness than permanent to transient (P2T) KDIs.
• Observed that P2T KDIs tend to be intra-module, potentially maintaining normal functions, while T2P KDIs are inter-module, possibly involved in signal transduction.

Conclusions:

• The study supports the hypothesis that dynamic switching of PPIs contributes to cancer development.
• Distinct network properties of P2T and T2P KDIs correlate with their proposed roles in normal function maintenance and signal transduction, respectively.
• Findings on KDIs provide a framework for understanding key aspects of cancer biology.