AET-net: A framework for subtype classification based on the multi-omics data of breast cancer
Abstract
Breast cancer (BC) is one of the most prevalent cancers worldwide and remains a significant global public health challenge. The biomechanical characteristics of tumor microenvironments provide critical insights into cellular interactions and mechanical stress responses that potentially influence cancer progression. The integration and analysis of multi-omics data for BC subtype classification present substantial challenges, including high-dimensional data complexity and difficulties in integrating heterogeneous omics data characteristics. To address these challenges, we propose an Autoencoder and Transformer integrated neural network (AET-net) classification framework. The experimental results demonstrate that our model achieves significant performance improvements in predicting BC subtypes based on integrated multi-omics datasets, with an Accuracy of 0.912 and an AUC of 0.9862. These results not only validate the high accuracy of our model in BC subtype classification, providing a valuable tool for diagnostic decision support, but also demonstrate the potential of integrated multi-omics data analysis in enhancing the precision and efficiency of BC subtype identification.
References
1. Akram M, Iqbal M, Daniyal M, Khan AU. (2017). Awareness and current knowledge of breast cancer. Biological research 50, 1–23
2. Momenimovahed Z, Salehiniya H. (2019). Epidemiological characteristics of and risk factors for breast cancer in the world. Breast Cancer: Targets and Therapy, 151–164
3. Łukasiewicz S, Czeczelewski M, Forma A, Baj J, Sitarz R, Stanisławek A. (2021). Breast cancer—epidemiology, risk factors, classification, prognostic markers, and current treatment strategies—an updated review. Cancers 13, 4287
4. Runel, G., Lopez-Ramirez, N., Chlasta, J., & Masse, I. (2021). Biomechanical properties of cancer cells. Cells 10(4), 887
5. McGhee, D. E., & Steele, J. R. (2020). Biomechanics of breast support for active women. Exercise and sport sciences reviews 48(3), 99-109
6. Tarchi, S. M., Pernia Marin, M., Hossain, M. M., & Salvatore, M. (2023). Breast stiffness, a risk factor for cancer and the role of radiology for diagnosis. Journal of Translational Medicine 21(1), 582
7. Shah, L., Latif, A., Williams, K. J., & Tirella, A. (2022). Role of stiffness and physico-chemical properties of tumour microenvironment on breast cancer cell stemness. Acta Biomaterialia 152, 273-289
8. Rajpal S, Rajpal A, Saggar A, Vaid AK, Kumar V, Agarwal M. Kumar N. (2023). XAI-MethylMarker: Explainable AI approach for biomarker discovery for breast cancer subtype classification using methylation data. Expert Systems with Applications 225, 120130
9. Rakha EA, Tse GM, Quinn CM. (2023). An update on the pathological classification of breast cancer. Histopathology 82(1), 5–16
10. Asleh K, Lluch A, Goytain A, Barrios C, Wang XQ, Torrecillas L, et al. (2023). Triple-negative pam50 non-basal breast cancer subtype predicts benefit from extended adjuvant capecitabine. Clinical Cancer Research 29, 389–400
11. Azevedo A L K, Gomig T H B, Batista M, et al. (2023). High-throughput proteomics of breast cancer subtypes: Biological characterization and multiple candidate biomarker panels to patients’ stratification. Journal of Proteomics 285, 104955
12. Orsini A, Diquigiovanni C, Bonora E. (2023). Omics technologies improving breast cancer research and diagnostics. International Journal of Molecular Sciences 24(16), 12690
13. Heo YJ, Hwa C, Lee GH, Park JM, An JY. (2021). Integrative multi-omics approaches in cancer research: from biological networks to clinical subtypes. Molecules and cells 44(7), 433–443
14. Wang, Z.-z., Li, X.-h., Wen, X.-l., Wang, N., Guo, Y., Zhu, X., et al. (2023). Integration of multi-omics data reveals a novel hybrid breast cancer subtype and its biomarkers. Frontiers in Oncology 13, 1130092
15. Choi, J. M. and Chae, H. (2023). mobrca-net: a breast cancer subtype classification framework based on multi-omics attention neural networks. BMC bioinformatics 24, 169
16. Zubair, M., Wang, S., and Ali, N. (2021). Advanced approaches to breast cancer classification and diagnosis. Frontiers in Pharmacology 11, 632079
17. Gao, F., Wang, W., Tan, M., Zhu, L., Zhang, Y., Fessler, E., et al. (2019). Deepcc: a novel deep learning-based framework for cancer molecular subtype classification. Oncogenesis 8, 44
18. Meti, N., Saednia, K., Lagree, A., Tabbarah, S., Mohebpour, M., Kiss, A., et al. (2021). Machine learning frameworks to predict neoadjuvant chemotherapy response in breast cancer using clinical and pathological features. JCO Clinical Cancer Informatics 5, 66–80
19. Graudenzi, A., Cava, C., Bertoli, G., Fromm, B., Flatmark, K., Mauri, G., et al. (2017). Pathway-based classification of breast cancer subtypes. Front Biosci 22, 1697–1712
20. Mohammed, A. J., Hassan, M. M., and Kadir, D. H. (2020). Improving classification performance for a novel imbalanced medical dataset using smote method. International Journal of Advanced Trends in Computer Science and Engineering 9, 3161–3172
21. Satpathi, S., Gaurkar, S. S., Potdukhe, A., and Wanjari, M. B. (2023). Unveiling the role of hormonal imbalance in breast cancer development: A comprehensive review. Cureus 15
22. Choi, S. R. and Lee, M. (2023). Transformer architecture and attention mechanisms in genome data analysis: a comprehensive review. Biology 12, 1033
23. Thennavan, A., Beca, F., Xia, Y., Garcia-Recio, S., Allison, K., Collins, L. C., et al. (2021). Molecular analysis of tcga breast cancer histologic types. Cell genomics 1
24. Huang, R., Soneson, C., Ernst, F. G., Rue-Albrecht, K. C., Yu, G., Hicks, S. C., et al. (2020). Treesummarizedexperiment: a s4 class for data with hierarchical structure. F1000Research 9
25. Parker, J. S., Mullins, M., Cheang, M. C., Leung, S., Voduc, D., Vickery, T., et al. (2009). Supervised risk predictor of breast cancer based on intrinsic subtypes. Journal of clinical oncology 27, 1160
26. Nielsen, T. O., Leung, S. C. Y., Rimm, D. L., Dodson, A., Acs, B., Badve, S., et al. (2021). Assessment of ki67 in breast cancer: updated recommendations from the international ki67 in breast cancer working group. JNCI: Journal of the National Cancer Institute 113, 808–819
27. Voineskos, S. H., Klassen, A. F., Cano, S. J., Pusic, A. L., and Gibbons, C. J. (2020). Giving meaning to differences in breast-q scores: minimal important difference for breast reconstruction patients. Plastic and reconstructive surgery 145, 11e–20e
28. Yan, R., Zhang, F., Rao, X., Lv, Z., Li, J., Zhang, L., et al. (2021). Richer fusion network for breast cancer classification based on multimodal data. BMC Medical Informatics and Decision Making 21, 1–15
29. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., et al. (2017). Attention is all you need. Advances in neural information processing systems 30
30. Kotsiantis, S. B., Zaharakis, I. D., and Pintelas, P. E. (2006). Machine learning: a review of classification and combining techniques. Artificial Intelligence Review 26, 159–190
Copyright (c) 2024 Qiaosheng Zhang, Yalong Wei, Jie Hou, Junjie Xu, Zhenyu Sun, Heng Zhang, Zhaoman Zhong
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
