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

Summary

This study applies a graph-theoretical framework to a modeled protein-protein interaction network of the RAS signaling pathway to identify centrally connected zone 1 proteins and characterize their functional and biological relevance.

Abstract

The RAS signaling pathway is a fundamental regulator of cellular growth, proliferation, and survival. Dysregulation of this pathway is strongly implicated in cancer development, yet systematic strategies for identifying which pathway proteins represent the most promising therapeutic targets remain limited. The rationale of this study was to investigate the diversity of central proteins within the RAS signaling pathway and assess their functional significance in cancer biology. To achieve this, we modeled the human protein-protein interaction network as a metric space using a graph-theoretical framework. Shortest-path distances were computed to identify the most central proteins, which were then classified into functional zones. Proteins located in zone 1, representing the most connected zone, were cross-referenced with curated RAS pathway datasets. Functional enrichment analysis, oncogene/tumor suppressor evaluation, and cancer genome data integration were used to interpret biological roles and therapeutic potential. The results revealed that 95.2% of central RAS proteins are involved in signaling, with 59.5% classified as essential. Key proteins such as BCL2L1, RAF1, RHOA, MAP2K1, EGFR, CDC42, and ANGPT1 were identified as central players in processes including apoptosis resistance, metastasis, angiogenesis, and tumor progression. Several of these proteins also showed strong associations with established oncogenes and successful therapeutic targets. In conclusion, this study demonstrates that central proteins in the RAS signaling pathway exhibit functional diversity that underpins their importance in cancer progression. These findings provide a reproducible network-based workflow for identifying pathway-relevant molecular candidates and contribute to the development of more precise, pathway-oriented cancer therapies.

Introduction

The RAS signaling pathway is a critical regulator of cellular communication and has been extensively studied due to its central role in oncogenesis¹. This pathway, composed of a series of interconnected proteins, transmits extracellular cues to the nucleus, thereby regulating key cellular processes such as proliferation, growth, and differentiation².

The discovery of RAS oncogenes originated from early studies on transforming retroviruses, beginning with the identification of the Harvey sarcoma virus in the 1960s, which demonstrated that viral genes could induce malignant transformation in mam....

Protocol

This study was based exclusively on the analysis of publicly available datasets and did not involve human participants, animal subjects, or tissue samples. Therefore, no institutional review board (IRB) or animal care and use committee (IACUC) approval was required.

Data collection and preprocessing
Human PPI data and curated RAS signaling pathway proteins were retrieved from publicly accessible repositories, including Genome Biology functional protein interaction datasets²³ and the Comparative Toxicogenomics Database (CTD) (https://ctdbase.org/). Datasets were standardized into tab-delimited t....

Results

In our previous work, we developed the zone-based methodology for modeling PPI networks as metric spaces¹⁷. This approach enabled the classification of proteins into hierarchical zones according to their distance from the central node; with zone 1 representing the most highly connected and functionally essential proteins. Building on this foundation, the present study specifically focuses on zone 1 proteins identified in our earlier work and investigates their roles within the RAS signaling pathwa.......

Discussion

This study presents a graph-theoretical framework for analyzing protein-protein interaction networks within the RAS signaling pathway to identify centrally connected proteins with functional relevance. By modeling the PPI network as a metric space and focusing on zone 1 proteins defined by minimal shortest-path distances, the analysis highlights proteins that occupy structurally central positions within the pathway. Proteins including BCL2L1, RAF1, RHOA, MAP2K1, EGFR, CDC42, and ANGPT1 were consistently classified within.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Human protein–protein interaction datasets	Public repositories (Genome Biology, BioGRID, IntAct)	Accession IDs (as cited in refs)	N/A
RAS signaling pathway protein list	Comparative Toxicogenomics Database	https://ctdbase.org/	RRID:SCR_006530
Python (Version 3.10 or higher)	Python Software Foundation (USA)	https://www.python.org/	RRID:SCR_008394
Boost Graph Library (C++)	Boost.org (USA)	https://www.boost.org/	N/A
Cytoscape (used for visualization)	Cytoscape Consortium	Version 3.9.1	RRID:SCR_003032
Large-scale cancer genome sequencing data	The Cancer Genome Atlas (TCGA)	https://www.cancer.gov/ccg/research/genome-sequencing/tcga	RRID:SCR_003193