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CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection

Author

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  • Hao Li

    (Academy of Military Medical Sciences)

  • Zebei Han

    (Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering)

  • Yu Sun

    (Academy of Military Medical Sciences)

  • Fu Wang

    (Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering)

  • Pengzhen Hu

    (Northwestern Polytechnical University)

  • Yuang Gao

    (PLA General Hospital, the Fifth Medical Center)

  • Xuemei Bai

    (Academy of Military Medical Sciences)

  • Shiyu Peng

    (Academy of Military Medical Sciences)

  • Chao Ren

    (Academy of Military Medical Sciences)

  • Xiang Xu

    (Academy of Military Medical Sciences)

  • Zeyu Liu

    (Academy of Military Medical Sciences)

  • Hebing Chen

    (Academy of Military Medical Sciences)

  • Yang Yang

    (Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering)

  • Xiaochen Bo

    (Academy of Military Medical Sciences)

Abstract

Cancer is rarely the straightforward consequence of an abnormality in a single gene, but rather reflects a complex interplay of many genes, represented as gene modules. Here, we leverage the recent advances of model-agnostic interpretation approach and develop CGMega, an explainable and graph attention-based deep learning framework to perform cancer gene module dissection. CGMega outperforms current approaches in cancer gene prediction, and it provides a promising approach to integrate multi-omics information. We apply CGMega to breast cancer cell line and acute myeloid leukemia (AML) patients, and we uncover the high-order gene module formed by ErbB family and tumor factors NRG1, PPM1A and DLG2. We identify 396 candidate AML genes, and observe the enrichment of either known AML genes or candidate AML genes in a single gene module. We also identify patient-specific AML genes and associated gene modules. Together, these results indicate that CGMega can be used to dissect cancer gene modules, and provide high-order mechanistic insights into cancer development and heterogeneity.

Suggested Citation

  • Hao Li & Zebei Han & Yu Sun & Fu Wang & Pengzhen Hu & Yuang Gao & Xuemei Bai & Shiyu Peng & Chao Ren & Xiang Xu & Zeyu Liu & Hebing Chen & Yang Yang & Xiaochen Bo, 2024. "CGMega: explainable graph neural network framework with attention mechanisms for cancer gene module dissection," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
  • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-50426-6
    DOI: 10.1038/s41467-024-50426-6
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