Integrated Somatic Mutation Network Diffusion Model for Stratification of Breast Cancer into Different Metabolic Mutation Subtypes

Dongqing Su; Honghao Li; Tao Wang; Min Zou; Haodong Wei; Yuqiang Xiong; Hongmei Sun; Shiyuan Wang; Qilemuge Xi; Yongchun Zuo; Lei Yang

doi:10.2174/0115748936298012240322091111

ISSN: 1574-8936
E-ISSN: 2212-392X

Integrated Somatic Mutation Network Diffusion Model for Stratification of Breast Cancer into Different Metabolic Mutation Subtypes
Authors: Dongqing Su¹, Honghao Li¹, Tao Wang¹, Min Zou¹, Haodong Wei¹, Yuqiang Xiong¹, Hongmei Sun¹, Shiyuan Wang¹, Qilemuge Xi², Yongchun Zuo^2,3,4 and Lei Yang¹
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¹ College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, 150081, China ; ² The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, 010070, China ; ³ Digital College, Inner Mongolia Intelligent Union Big Data Academy, Inner Mongolia Wesure Date Technology Co., Ltd. Hohhot, 010010, China ; ⁴ Inner Mongolia International Mongolian Hospital, Hohhot, 010065, China
Source: Current Bioinformatics, Volume 20, Issue 3, Mar 2025, p. 246 - 256
DOI: https://doi.org/10.2174/0115748936298012240322091111
- Received: 13 Dec 2023
- Accepted: 12 Mar 2024
- Available online: 08 Apr 2024

Abstract

Background

Mutations in metabolism-related genes in somatic cells potentially lead to disruption of metabolic pathways, which results in patients exhibiting different molecular and pathological features.

Objective

In this study, we focused on somatic mutation data to investigate the significance of metabolic mutation typing in guiding the prognosis and treatment of breast cancer patients.

Methods

The somatic mutation profile of breast cancer patients was analyzed and smoothed by utilizing a network diffusion model within the protein-protein interaction network to construct a comprehensive somatic mutation network diffusion profile. Subsequently, a deep clustering approach was employed to explore metabolic mutation typing in breast cancer based on integrated metabolic pathway information and the somatic mutation network diffusion profile. In addition, we employed deep neural networks and machine learning prediction models to assess the feasibility of predicting drug responses through somatic mutation network diffusion profiles.

Results

Significant differences in prognosis and metabolic heterogeneity were observed among the different metabolic mutation subtypes, characterized by distinct alterations in metabolic pathways and genetic mutations, and these mutational features offered potential targets for subtype-specific therapies. Furthermore, there was a strong consistency between the results of the drug response prediction model constructed on the somatic mutation network diffusion profile and the actual observed drug responses.

Conclusion

Metabolic mutation typing of cancer assists in guiding patient prognosis and treatment.

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Integrated Somatic Mutation Network Diffusion Model for Stratification of Breast Cancer into Different Metabolic Mutation Subtypes

Curr Bioinform 20, 246 (2025); https://doi.org/10.2174/0115748936298012240322091111

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Supplements

Supplementary material is available on the publisher’s website along with the published article.

Article Type: Research Article

Keyword(s): Breast cancer; deep clustering; drug response prediction; metabolic pathway; network diffusion; somatic mutation

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Integrated Somatic Mutation Network Diffusion Model for Stratification of Breast Cancer into Different Metabolic Mutation Subtypes

Abstract

Supplementary material is available on the publisher’s website along with the published article.

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