Graph-theoretical Analysis of a Modeled Protein-protein Interaction Network in the RAS Signaling Pathway

Area of Science:

• Molecular Biology
• Systems Biology
• Oncology

Background:

• The RAS signaling pathway regulates cell growth, proliferation, and survival.
• Dysregulation of this pathway is a key driver in cancer development.
• Systematic identification of therapeutic targets within this pathway is limited.

Purpose of the Study:

• Investigate the diversity of central proteins in the RAS signaling pathway.
• Assess the functional significance of these proteins in cancer biology.
• Identify potential therapeutic targets for cancer treatment.

Main Methods:

• Modeled the human protein-protein interaction network as a metric space using graph theory.
• Computed shortest-path distances to identify central proteins.
• Cross-referenced central proteins with RAS pathway datasets and cancer genome data.

Main Results:

• Identified central RAS proteins involved in signaling (95.2%) and essential functions (59.5%).
• Highlighted key proteins like BCL2L1, RAF1, RHOA, MAP2K1, EGFR, CDC42, and ANGPT1.
• Demonstrated associations of these proteins with oncogenes and therapeutic targets.

Conclusions:

• Central RAS pathway proteins exhibit functional diversity critical for cancer progression.
• Developed a network-based workflow for identifying pathway-relevant molecular candidates.
• Findings support the development of precise, pathway-oriented cancer therapies.