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Updated: Jun 14, 2025
Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
Published on: July 22, 2020
Multiview representation learning for identification of novel cancer genes and their causative biological mechanisms
Jianye Yang1, Haitao Fu1,2, Feiyang Xue1
1College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.
Abstract:
Tumorigenesis arises from the dysfunction of cancer genes, leading to uncontrolled cell proliferation through various mechanisms. Establishing a complete cancer gene catalogue will make precision oncology possible. Although existing methods based on graph neural networks (GNN) are effective in identifying cancer genes, they fall short in effectively integrating data from multiple views and interpreting predictive outcomes. To address these shortcomings, an interpretable representation learning framework IMVRL-GCN is proposed to capture both shared and specific representations from multiview data, offering significant insights into the identification of cancer genes. Experimental results demonstrate that IMVRL-GCN outperforms state-of-the-art cancer gene identification methods and several baselines. Furthermore, IMVRL-GCN is employed to identify a total of 74 high-confidence novel cancer genes, and multiview data analysis highlights the pivotal roles of shared, mutation-specific, and structure-specific representations in discriminating distinctive cancer genes. Exploration of the mechanisms behind their discriminative capabilities suggests that shared representations are strongly associated with gene functions, while mutation-specific and structure-specific representations are linked to mutagenic propensity and functional synergy, respectively. Finally, our in-depth analyses of these candidates suggest potential insights for individualized treatments: afatinib could counteract many mutation-driven risks, and targeting interactions with cancer gene SRC is a reasonable strategy to mitigate interaction-induced risks for NR3C1, RXRA, HNF4A, and SP1.
Insights
This study introduces IMVRL-GCN, an interpretable framework for cancer gene identification using multiview data. It successfully identifies novel cancer genes and offers insights for personalized cancer treatments.
Area of Science:
- Genomics
- Computational Biology
- Precision Oncology
Background:
- Tumorigenesis results from cancer gene dysfunction, driving uncontrolled cell proliferation.
- A comprehensive cancer gene catalog is crucial for advancing precision oncology.
- Existing graph neural network (GNN) methods struggle with integrating multiview data and interpretability in cancer gene identification.
Purpose of the Study:
- To develop an interpretable representation learning framework, IMVRL-GCN, for enhanced cancer gene identification.
- To effectively integrate and analyze shared and specific representations from multiview data.
- To provide insights into the mechanisms of cancer gene discrimination and potential therapeutic strategies.
Main Methods:
- Proposed an interpretable representation learning framework named IMVRL-GCN.
- Utilized multiview data to capture shared and specific gene representations.
- Compared IMVRL-GCN performance against state-of-the-art methods and baselines.
Main Results:
- IMVRL-GCN demonstrated superior performance compared to existing cancer gene identification methods.
- Identified 74 high-confidence novel cancer genes.
- Multiview data analysis revealed the importance of shared, mutation-specific, and structure-specific representations in cancer gene discrimination.
Conclusions:
- Shared representations correlate with gene function, while mutation-specific and structure-specific representations relate to mutagenic propensity and functional synergy.
- Identified potential therapeutic strategies, including using afatinib for mutation-driven risks and targeting SRC interactions for specific genes.
- The findings offer valuable insights for developing individualized cancer treatments.
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