Identification of breast cancer subtypes based on graph convolutional network_Journal of Biomedical Engineering

Authors：

AN Yishuai ¹ ,  LIU Xiaojun ¹ , CHEN Hengling ¹ , WAN Guihong ²

1. School of Biomedical Engineering, South-Central Minzu University, Wuhan 430074, P. R. China;
2. Department of Dermatology, Massachusetts General Hospital, Harvard University, Boston 02138, USA;

Corresponding author：

LIU Xiaojun, Email: liuxj@mail.scuec.edu.cn

Keywords：

Breast cancer; Deep learning; Graph convolutional network; Subtype classification

DOI：

10.7507/1001-5515.202306071

Video：

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Identification of molecular subtypes of malignant tumors plays a vital role in individualized diagnosis, personalized treatment, and prognosis prediction of cancer patients. The continuous improvement of comprehensive tumor genomics database and the ongoing breakthroughs in deep learning technology have driven further advancements in computer-aided tumor classification. Although the existing classification methods based on gene expression omnibus database take the complexity of cancer molecular classification into account, they ignore the internal correlation and synergism of genes. To solve this problem, we propose a multi-layer graph convolutional network model for breast cancer subtype classification combined with hierarchical attention network. This model constructs the graph embedding datasets of patients’ genes, and develops a new end-to-end multi-classification model, which can effectively recognize molecular subtypes of breast cancer. A large number of test data prove the good performance of this new model in the classification of breast cancer subtypes. Compared to the original graph convolutional neural networks and two mainstream graph neural network classification algorithms, the new model has remarkable advantages. The accuracy, weight-F1-score, weight-recall, and weight-precision of our model in seven-category classification has reached 0.851 7, 0.823 5, 0.851 7 and 0.793 6 respectively. In the four-category classification, the results are 0.928 5, 0.894 9, 0.928 5 and 0.865 0 respectively. In addition, compared with the latest breast cancer subtype classification algorithms, the method proposed in this paper also achieved the highest classification accuracy. In summary, the model proposed in this paper may serve as an auxiliary diagnostic technology, providing a reliable option for precise classification of breast cancer subtypes in the future and laying the theoretical foundation for computer-aided tumor classification.

Citation： AN Yishuai, LIU Xiaojun, CHEN Hengling, WAN Guihong. Identification of breast cancer subtypes based on graph convolutional network. Journal of Biomedical Engineering, 2024, 41(1): 121-128. doi: 10.7507/1001-5515.202306071 Copy

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29.	El-Nabawy A, Belal N A, El-Bendary N. A cascade deep forest model for breast cancer subtype classification using multi-omics data. Mathematics, 2021, 9(13): 1574.
30.	Mohaiminul Islam M, Huang S, Ajwad R, et al. An integrative deep learning framework for classifying molecular subtypes of breast cancer. Computational and Structural Biotechnology Journal, 2020, 18: 2185-2199.
31.	Chen R, Yang L, Goodison S, et al. Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data. Bioinformatics, 2020, 36(5): 1476-1483.

1. Sung H, Ferlay J, Siegel R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
2. 蒲星月, 马原, 钟志刚. 2006-2020年中国女性乳腺癌死亡趋势分析——基于年龄-时期-出生队列模型. 卫生经济研究, 2023, 40(2): 28-33.
3. Perou C M, Therese Sørlie, Eisen M B, et al. Molecular portraits of human breast tumours. Nature, 2000, 490(6797): 747-752.
4. Parker J S, Mullins M, Cheang M C, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. Journal of Clinical Oncology, 2009, 27(8): 1160-1167.
5. Fan C, Oh D S, Wessels L, et al. Concordance among gene-expression–based predictors for breast cancer. New England Journal of Medicine, 2006, 355(6): 560-569.
6. Lin C Y, Ruan P, Li R, et al. Deep learning with evolutionary and genomic profiles for identifying cancer subtypes. Journal of Bioinformatics and Computational Biology, 2019, 17(3): 1940005.
7. Magaki S, Hojat S A, Wei B, et al. An introduction to the performance of immunohistochemistry. Methods Mol Biol, 2019, 1897: 289-298.
8. 黄军豪, 廖天驰. 基于深度学习的乳腺癌分子亚型分类研究. 现代计算机, 2020(22): 3-8.
9. Waks A G, Winer E P. Breast cancer treatment: a review. JAMA, 2019, 321(3): 288-300.
10. 颜锐, 陈丽萌, 李锦涛, 等. 基于深度学习和组织病理图像的癌症分类研究进展. 协和医学杂志, 2021, 12(5): 742-748.
11. 雪峰豪, 蒋海波, 唐聃. 深度学习在健康医疗中的应用研究综述. 计算机科学, 2023, 50(4): 1-15.
12. Alharbi F, Vakanski A. Machine learning methods for cancer classification using gene expression data: a review. Bioengineering, 2023, 10(2): 173.
13. Gao F, Wang W, Tan M, et al. DeepCC: a novel deep learning-based framework for cancer molecular subtype classification. Oncogenesis, 2019, 8(9): 44.
14. Mostavi M, Chiu Y C, Huang Y, et al. Convolutional neural network models for cancer type prediction based on gene expression. BMC Medical Genomics, 2020, 13(Suppl 5): 44.
15. Rhee S , Seo S , Kim S . Hybrid approach of relation network and localized graph convolutional filtering for breast cancer subtype classification. arXiv preprint, 2017. arXiv.1711.05859.
16. Lee S, Lim S, Lee T, et al. Cancer subtype classification and modeling by pathway attention and propagation. Bioinformatics, 2020, 36(12): 3818-3824.
17. Li B, Wang T, Nabavi S. Cancer molecular subtype classification by graph convolutional networks on multi-omics data//Proceedings of the 12th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics, Gainesville Florida: SIGBIOM, 2021(50): 1-9.
18. Kipf T N , Welling M. Semi-supervised classification with graph convolutional networks. arXiv preprint, 2017. arXiv.1609.02907.
19. Curtis C, Shah S P, Chin S F, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature, 2012, 486(7403): 346-352.
20. Pereira B, Chin S F, Rueda O M, et al. The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes. Nature Communications, 2016, 7: 11479.
21. Gao J, Aksoy B A, Dogrusoz U, et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Science Signaling, 2013, 6(269): p11.
22. Szklarczyk D, Franceschini A, Wyder S, et al. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Research, 2015, 43(D1): D447-D452.
23. Lee J, Lee I, Kang J. Self-attention graph pooling//36th International Conference on Machine Learning (ICML 2019), California: International Machine Learning Society (IMLS), 2019: 6661-6670.
24. Fey M, Lenssen J E. Fast graph representation learning with PyTorch geometric. arXiv preprint, 2019. arXiv.1903.02428.
25. Pedregosa F, Varoquaux G, Gramfort A, et al. Scikit-learn: machine learning in Python. JMLR, 2011(12): 2825-2830.
26. Holliday D L, Speirs V. Choosing the right cell line for breast cancer research. Breast Cancer Research, 2011, 13(4): 215.
27. Prat A, Parker J S, Karginova O, et al. Phenotypic and molecular characterization of the claudin-low intrinsic subtype of breast cancer. Breast Cancer Research, 2010, 12(5): R68.
28. Lupat R, Perera R, Loi S, et al. Moanna: multi-omics autoencoder-based neural network algorithm for predicting breast cancer subtypes. IEEE Access, 2023, 11: 10912-10924.
29. El-Nabawy A, Belal N A, El-Bendary N. A cascade deep forest model for breast cancer subtype classification using multi-omics data. Mathematics, 2021, 9(13): 1574.
30. Mohaiminul Islam M, Huang S, Ajwad R, et al. An integrative deep learning framework for classifying molecular subtypes of breast cancer. Computational and Structural Biotechnology Journal, 2020, 18: 2185-2199.
31. Chen R, Yang L, Goodison S, et al. Deep-learning approach to identifying cancer subtypes using high-dimensional genomic data. Bioinformatics, 2020, 36(5): 1476-1483.

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