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Background <p>Protein–protein interactions (PPIs) are fundamental components of signal transduction, and identifying dysregulated pathways is essential for understanding disease mechanisms. Conventional methods use pathway maps and cross‑sectional gene expression data to define sub‑pathways in advance, but this requirement becomes impractical as pathway complexity increases. Herein, rather than attempting to predefine all sub-pathways, we propose an alternative method whereby (1) each PPI constituting pathways is quantitatively evaluated in terms of the extent of aberration, and (2) dysregulated sub-pathways are subsequently explored based on these evaluations.</p> Methods <p>To quantitatively evaluate the degree of aberration for each PPI, we constructed a mathematical model, assuming a balance between association and dissociation reactions. The extent of aberration was assessed through a model parameter defined as the difference in signal intensity between diseased and healthy groups, with consideration of protein levels. The proposed method was applied to publicly available data, including the mTOR signalling pathway map and two gene expression datasets—one from clear cell renal cell carcinoma and the other from lung squamous cell carcinoma. A simulation study was also conducted to evaluate its performance.</p> Results <p>The proposed method identified PPIs that were also deemed aberrant by HiPathia, the best-performing conventional method, supporting its validity. In addition, our method explored sub-pathways that may be overlooked by predefined approaches, such as HiPathia. Furthermore, a simulation study indicated that the method exhibited sufficient performance for real-world application.</p> Conclusion <p>Although our method relies on several strong assumptions, these findings demonstrate that it provides a novel framework for pathway analysis, applicable even to complex pathways when these assumptions are satisfied.</p> Graphical Abstract <p></p>

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Model-based quantification of protein–protein interaction aberrations for exploring dysregulated signalling pathways through pathway maps and gene expression levels

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月8日

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  • Kenta Kevee Kisaï,
  • Takashi Omori

摘要

Background

Protein–protein interactions (PPIs) are fundamental components of signal transduction, and identifying dysregulated pathways is essential for understanding disease mechanisms. Conventional methods use pathway maps and cross‑sectional gene expression data to define sub‑pathways in advance, but this requirement becomes impractical as pathway complexity increases. Herein, rather than attempting to predefine all sub-pathways, we propose an alternative method whereby (1) each PPI constituting pathways is quantitatively evaluated in terms of the extent of aberration, and (2) dysregulated sub-pathways are subsequently explored based on these evaluations.

Methods

To quantitatively evaluate the degree of aberration for each PPI, we constructed a mathematical model, assuming a balance between association and dissociation reactions. The extent of aberration was assessed through a model parameter defined as the difference in signal intensity between diseased and healthy groups, with consideration of protein levels. The proposed method was applied to publicly available data, including the mTOR signalling pathway map and two gene expression datasets—one from clear cell renal cell carcinoma and the other from lung squamous cell carcinoma. A simulation study was also conducted to evaluate its performance.

Results

The proposed method identified PPIs that were also deemed aberrant by HiPathia, the best-performing conventional method, supporting its validity. In addition, our method explored sub-pathways that may be overlooked by predefined approaches, such as HiPathia. Furthermore, a simulation study indicated that the method exhibited sufficient performance for real-world application.

Conclusion

Although our method relies on several strong assumptions, these findings demonstrate that it provides a novel framework for pathway analysis, applicable even to complex pathways when these assumptions are satisfied.

Graphical Abstract