Integrating multi-omics data through deep learning for accurate cancer prognosis prediction
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Abstract
Background Genomic information is nowadays widely used for precise cancer treatments. Since the individual type of omics data only represents a single view that suffers from data noise and bias, multiple types of omics data are required for accurate cancer prognosis prediction. However, it is challenging to effectively integrate multi-omics data due to the large number of redundant variables but relatively small sample size. With the recent progress in deep learning techniques, Autoencoder was used to integrate multi-omics data for extracting representative features. Nevertheless, the generated model is fragile from data noises. Additionally, previous studies usually focused on individual cancer types without making comprehensive tests on pan-cancer. Here, we employed the denoising Autoencoder to get a robust representation of the multi-omics data, and then used the learned representative features to estimate patients' risks.Results By applying to 15 cancers from The Cancer Genome Atlas (TCGA), our method was shown to improve the C-index values over previous methods by 6.5% on average. Considering the difficulty to obtain multi-omics data in practice, we further used only mRNA data to fit the estimated risks by training XGboost models, and found the models could achieve an average C-index value of 0.627. As a case study, the breast cancer prognosis prediction model was independently tested on three datasets from the Gene Expression Omnibus (GEO), and shown able to significantly separate high-risk patients from low-risk ones (C-index>0.6, p-values<0.05). Based on the risk subgroups divided by our method, we identified nine prognostic markers highly associated with breast cancer, among which seven genes have been proved by literature review.Conclusion Our comprehensive tests indicated that we have constructed an accurate and robust framework to integrate multi-omics data for cancer prognosis prediction. Moreover, it is an effective way to discover cancer prognosis-related genes.
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Many methods have shown unequal contribution of different modalities to predictive performance.
“Questioning this assumption is relevant as many methods have shown unequal contribution of different modalities to the predictive performance (see for example [36, 42]).”
From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [36]
Different modalities can add information to different degrees.
“However, different modalities can add information to different degrees (e.g. [36, 44]).”
From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [36]
Early nonlinear fusion methods can outperform shallow methods on prediction tasks.
“The applications of early nonlinear fusion methods reviewed here have shown that these methods can outperform shallow methods on prediction tasks (e.g. [35, 36]).”
From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [36]
For the same clinical task, a similar workflow has been adapted by researchers, but applying denoising AEs instead.
“For the same clinical task, a similar workflow has been adapted by researchers, but applying denoising AEs (DAEs) [91] instead [35, 36].”
From Multimodal deep learning for biomedical data fusion: a review (2022) · cites this paper as [36]
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- Authors
- Chai, H., Zhou, X., Zhang, Z., Rao, J., Zhao, H., & Yang, Y.
- Affiliations
- Sun Yat-sen University (CN)
- Corresponding author
- Zhao, Huiying
- Yang, Yuedong
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2021-07
- Volume
- 134
- Pages
- 104481
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W3163443268
- Cited by
- 194
- References
- 40
- Citations by year
- 2026: 37 · 2025: 52 · 2024: 43 · 2023: 35 · 2022: 18 · 2021: 9
- FWCI
- 8.23
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed (publishedVersion)
- Griffith Research Online (submittedVersion, cc-by-nc-nd)
- Funders
- National High-tech Research and Development Program (2020YFB0204803)
- National Key Research and Development Program of China
- Natural Science Foundation of Guangdong Province (2019A1515012207)
- National Natural Science Foundation of China (61772566, 81801132)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Keywords
- multi-omics integration, cancer prognosis prediction, denoising autoencoder, pan-cancer, breast cancer, mRNA expression, risk stratification, XGBoost, TCGA
- MeSH terms
- Deep Learning, Breast Neoplasms, Female, Humans, Oncogenes, Gene Expression Profiling, Genomics
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