Research Article

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

DOI:

10.3791/69681

⸱

March 13th, 2026

 , 
1Department of Mathematics & Statistics, College of Science, King Faisal University, 2Department of Mathematics & Applied Mathematics, University of the Western Cape

In This Article

Summary

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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

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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

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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 mammalian cells3. This concept was later extended to human cancer when Der and colleagues showed that the transforming genes identified in human bladder and lung carcinoma cell lines were homologous to the Harvey and Kirsten sarcoma virus oncogenes (HRAS and KRAS), thereby establishing RAS genes as bona fide cellular oncogenes implicated in human malignancies4. Subsequent reviews have summarized these discoveries and their implications for cancer biology5.

Beyond oncology, dysregulated RAS/MAPK signaling has also been implicated in cardiovascular disorders, including congenital heart disease, cardiomyopathies, and vascular abnormalities, underscoring the pleiotropic roles of this pathway across diverse physiological and pathological contexts6,7.

Furthermore, RASopathies have been shown to cause significant cardiac manifestations, linking dysregulated RAS/MAPK signaling to congenital and acquired cardiovascular diseases8,9. In addition, studies of small GTPases such as RhoA highlight their role in cardiac hypertrophy and cardioprotection under stress conditions10,12. These findings underscore the broader physiological importance of RAS signaling outside of cancer biology and justify further exploration to develop targeted therapeutic interventions in diverse disease contexts.

Several computational and bioinformatic studies have previously explored the RAS signaling pathway using network-based, enrichment, and systems biology approaches. These studies have focused on pathway reconstruction, mutation-driven network perturbations, signaling crosstalk, and drug response prediction13,14,15. While informative, most of these approaches restrict analysis to predefined pathway boundaries or rely on local network measures. In contrast, the present study models the entire human protein-protein interaction network as a metric space and identifies central proteins prior to pathway mapping, thereby providing a complementary, structure-driven perspective on RAS signaling organization.

Graph-theoretical models provide an innovative framework for understanding complex biological networks. By conceptualizing the protein-protein interaction (PPI) network as a metric space, proteins can be classified into zones according to their distance from the network center17. Proteins in zone 1, which represent the most central and highly connected nodes, are often essential for cell viability and disproportionately involved in disease-related pathways18. Previous network-based studies have demonstrated that central and highly connected proteins exert a disproportionate influence on network stability, information flow, and disease susceptibility, and are frequently enriched among essential genes and clinically relevant targets19,20,21,22. However, few studies have applied this framework specifically to the RAS signaling pathway, leaving a gap in our understanding of how network centrality relates to RAS-mediated oncogenesis. The current study addresses this gap by systematically identifying central proteins within the RAS pathway and evaluating their roles in cancer biology. This approach not only enhances reproducibility by providing a clear computational protocol but also highlights novel targets for cancer therapies, thereby advancing the integration of systems biology with translational oncology.

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Protocol

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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 text files to ensure uniformity. Preprocessing included the removal of duplicate interactions, the elimination of self-loops, and the filtering of non-human entries. A complete list of all proteins included in the standardized tab-delimited input file is provided in Supplementary Table S1.

Network construction
The human PPI network was modeled as a graph in which proteins represent nodes and their interactions represent edges. To compute shortest-path distances, Dijkstra's algorithm was implemented using a custom Python script integrated with the Boost Graph Library (C++) (http://www.boost.org/). The central node was defined as the protein with the smallest maximum shortest-path distance to all others. The network was then partitioned into zones based on distance from this central protein.

Zone classification and pathway mapping
Proteins were classified into hierarchical zones (zone 1, zone 2, etc.) according to their distance from the central protein. Zone 1, containing the most central and highly connected proteins, was selected for analysis. Proteins in zone 1 were cross-referenced with curated RAS signaling pathway protein lists. All computational procedures employed in this study follow the framework previously described by Fadhal et al.17, with the present work focusing specifically on the biological interpretation of RAS-associated proteins within the first hierarchical network zone.

Functional enrichment analysis
Functional enrichment analysis was carried out using CTD annotations alongside curated pathway databases, with statistical significance defined by a corrected P-value cutoff of 0.01.

Oncogene and tumor suppressor evaluation
Central proteins overlapping with the RAS signaling pathway were further examined for their oncogenic or tumor suppressor roles. Data were extracted from large-scale cancer genome sequencing projects and validated against cancer-related databases. Proteins were classified into oncogenes, tumor suppressors, apoptosis-related proteins, or therapeutic targets based on published evidence24,25.

Reproducibility and validation
All computational analyses were repeated in triplicate using independent PPI datasets. These procedures, including all protocol steps and implementation details, were performed following the methodology described in our previously published work17. The results were consistent across independent runs, confirming the robustness and reproducibility of the protocol. To ensure reproducibility, each step of the workflow has been reviewed and described with sufficient detail to clarify how the analysis is performed. The complete workflow, including source code and datasets, is provided in the Supplementary Data S4. All analyses can be reproduced on a standard workstation without the need for specialized hardware.

Workflow schematic
An overall schematic of the study design is provided (Figure 1). This diagram summarizes each step of the protocol: 1) Data collection and preprocessing, 2) Network construction, 3) Zone classification and mapping, 4) Functional enrichment analysis, 5) Oncogene/tumor suppressor evaluation, and 6) Visualization of results.

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Results

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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...

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Discussion

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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...

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Disclosures

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All authors declare that there are no conflicts of interest related to this work.

Acknowledgements

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This work was supported by the Deanship of Scientific Research, Vice Presidency for Graduate Studies and Scientific Research, King Faisal University, Saudi Arabia KFU260567.

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
Human protein–protein interaction datasetsPublic repositories (Genome Biology, BioGRID, IntAct)Accession IDs (as cited in refs)N/A
RAS signaling pathway protein listComparative Toxicogenomics Databasehttps://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 ConsortiumVersion 3.9.1RRID:SCR_003032
Large-scale cancer genome sequencing dataThe Cancer Genome Atlas (TCGA)https://www.cancer.gov/ccg/research/genome-sequencing/tcgaRRID:SCR_003193

References

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,
  1. Luo, X., et al. Novel paradigms in KRAS targeting: unveiling strategies to combat drug resistance. Chin Med J. 138 (18), 2243-2267 (2025).
  2. Overmeyer, J. H., Maltese, W. A. Death pathways triggered by activated Ras in cancer cells. Front Biosci (Landmark Ed). 16 (5), 1693-1713 (2011).
  3. Harvey, J. J. An unidentified virus which causes the rapid production of tumours in mice. Nature. 204 (4963), 1104-1105 (1964).
  4. Krontiris, T. G., Cooper, G. M. Transforming genes of human bladder and lung carcinoma cell lines are homologous to the ras genes of Harvey and Kirsten sarcoma viruses. Proc Natl Acad Sci U S A. 79 (11), 3637-3640 (1982).
  5. Malumbres, M., Barbacid, M. RAS oncogenes: the first 30 years. Nat Rev Cancer. 3 (6), 459-465 (2003).
  6. Ramos-Kuri, M., et al. Molecules linked to Ras signaling as therapeutic targets in cardiac pathologies. Biol Res. 54 (1), 23(2021).
  7. Hilal, N., et al. RASopathies and cardiac manifestations. Front Cardiovasc Med. 10, 1176828(2023).
  8. Chennappan, S., et al. RASopathies in cardiac disease. Annu Rev Med. 76, 301-314 (2025).
  9. Hilal, N., et al. RASopathies and cardiac manifestations. Front Cardiovasc Med. 10, 1176828(2023).
  10. Wang, X., et al. Roles of small GTPases in cardiac hypertrophy. Mol Med Rep. 29 (2), 13332(2024).
  11. Miyamoto, S. Untangling the role of RhoA in the heart: protective effect and mechanism. Cell Death Dis. 15, 579(2024).
  12. Pang, J. M., et al. MicroRNA-708 emerges as a potential candidate to target undruggable NRAS. PLoS One. 18 (4), e0284744(2023).
  13. Adariani, S. R., et al. A comprehensive analysis of RAS-effector interactions reveals interaction hotspots and new binding partners. J Biol Chem. , (2021).
  14. Tang, R., et al. Multiplexed screens identify RAS paralogues HRAS and NRAS as suppressors of KRAS-driven lung cancer growth. Nat Cell Biol. 25 (1), 159-169 (2023).
  15. Montero-Bullón, J. F., et al. Integrated in silico MS-based phosphoproteomics and network enrichment analysis of RASopathy proteins. Orphanet J Rare Dis. 16 (1), 303(2021).
  16. Fadhal, E., Gamieldien, J., Mwambene, E. C. Protein interaction networks as metric spaces: a novel perspective on distribution of hubs. BMC Syst Biol. 8 (1), 1(2014).
  17. Fadhal, E., Mwambene, E. C., Gamieldien, J. Modelling human protein interaction networks as metric spaces has potential in disease research and drug target discovery. BMC Syst Biol. 8 (1), 2(2014).
  18. Viacava Follis, A. Centrality of drug targets in protein networks. BMC Bioinformatics. 22 (1), 527(2021).
  19. Rout, T., et al. Centrality measures and their applications in network analysis: unveiling important elements and their impact. Procedia Comput Sci. 235, 2756-2765 (2024).
  20. Barabási, A. L., Gulbahce, N., Loscalzo, J. Network medicine: a network-based approach to human disease. Nat Rev Genet. 12 (1), 56-68 (2011).
  21. Peng, Q., Schork, N. J. Utility of network integrity methods in therapeutic target identification. Front Genet. 5, 12(2014).
  22. Wu, G., Feng, X., Stein, L. A human functional protein interaction network and its application to cancer data analysis. Genome Biol. 11 (5), R53(2010).
  23. Bult, C. J., et al. The mouse genome database: genotypes, phenotypes, and models of human disease. Nucleic Acids Res. 41 (D1), D885-D891 (2012).
  24. Vogelstein, B., et al. Cancer genome landscapes. Science. 339 (6127), 1546-1558 (2013).
  25. Mizuno, S., et al. High-throughput functional evaluation of MAP2K1 variants in cancer. Mol Cancer Ther. 22 (2), 227-239 (2023).
  26. He, W., et al. A bioinformatic and mechanistic study elicits the antifibrotic effect of ursolic acid through the attenuation of oxidative stress with the involvement of ERK, PI3K/Akt, and p38 MAPK signaling pathways in human hepatic stellate cells and rat liver. Drug Des Devel Ther. 9, 3989-4104 (2015).
  27. Liotta, L., Petricoin, E. Molecular profiling of human cancer. Nat Rev Genet. 1 (1), 48-56 (2000).
  28. Schubbert, S., et al. Hyperactive Ras in developmental disorders and cancer. Nat Rev Cancer. 7 (4), 295-308 (2007).
  29. Cox, A. D., et al. Convergence of targeted and immune therapies for the treatment of oncogene-driven cancers. Cancer Discov. 13 (1), 19-22 (2023).
  30. Vasan, N., et al. At a crossroads: how to translate the roles of PI3K in oncogenic and metabolic signalling into improvements in cancer therapy. Nat Rev Clin Oncol. 19, 471-485 (2022).
  31. Czarnecka, A. M., et al. Targeted therapy in melanoma and mechanisms of resistance. Int J Mol Sci. 21, 4576(2020).
  32. Sahin, I., et al. Liquid biopsy: novel perspectives on the importance and spectrum of PIK3CA, PTEN and RET mutations in solid tumors. Mol Clin Oncol. 16, 1(2022).
  33. Haefliger, S., et al. Prevalence of molecular alterations in a Swiss cohort of 512 colorectal carcinoma patients by targeted next-generation sequencing analysis in routine diagnostics. Pathobiology. 90 (3), 166-175 (2023).
  34. Amato, O., et al. PIK3CA copy-number gain and inhibitors of the PI3K/AKT/mTOR pathway in triple-negative breast cancer. Cold Spring Harb Mol Case Stud. 9 (2), a006255(2023).
  35. Mizuno, S., et al. High-throughput functional evaluation of MAP2K1 variants in cancer. Mol Cancer Ther. 22 (2), 227-239 (2023).
  36. Haefliger, S., et al. Prevalence of molecular alterations in a Swiss cohort of 512 colorectal carcinoma patients by targeted next-generation sequencing analysis in routine diagnostics. Pathobiology. 90 (3), 166-175 (2023).

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Tags

RAS Signaling PathwayProtein Protein InteractionGraph Theoretical AnalysisCentral ProteinsCancer ProgressionFunctional EnrichmentTherapeutic TargetsOncogene EvaluationNetwork ModelingApoptosis Resistance

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