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

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 () and Xiaochen Bo ()
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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

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-50426-6

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