Group lasso regularized deep learning for cancer prognosis from multi-omics and clinical features
Open accessAbstract
Accurate prognosis of patients with cancer is important for the stratification of patients, the optimization of treatment strategies, and the design of clinical trials. Both clinical features and molecular data can be used for this purpose, for instance, to predict the survival of patients censored at specific time points. Multi-omics data, including genome-wide gene expression, methylation, protein expression, copy number alteration, and somatic mutation data, are becoming increasingly common in cancer studies. To harness the rich information in multi-omics data, we developed GDP (Group lass regularized Deep learning for cancer Prognosis), a computational tool for survival prediction using both clinical and multi-omics data. GDP integrated a deep learning framework and Cox proportional hazard model (CPH) together, and applied group lasso regularization to incorporate gene-level group prior knowledge into the model training process. We evaluated its performance in both simulated and real data from The Cancer Genome Atlas (TCGA) project. In simulated data, our results supported the importance of group prior information in the regularization of the model. Compared to the standard lasso regularization, we showed that group lasso achieved higher prediction accuracy when the group prior knowledge was provided. We also found that GDP performed better than CPH for complex survival data. Furthermore, analysis on real data demonstrated that GDP performed favorably against other methods in several cancers with large-scale omics data sets, such as glioblastoma multiforme, kidney renal clear cell carcinoma, and bladder urothelial carcinoma. In summary, we demonstrated that GDP is a powerful tool for prognosis of patients with cancer, especially when large-scale molecular features are available.
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Both the Xie et al. (2019) and Xu et al. (2019) methods require complete input, and initial imputation is performed in both cases to accommodate.
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From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Xie et al., 2019
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BackgroundMissing data in multi-omics integration: Recent advances through artificial intelligence
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“Xie et al. (2019) use deep learning to perform survival analysis, which involves feature selection by removing observations with high missingness, early concatenation of raw features to form the input to their model, and implicit feature extraction using deep neural networks, as the value at each…”
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- Authors
- Xie, G., Dong, C., Kong, Y., Zhong, J. F., Li, M., & Wang, K.
- Affiliations
- Children's Hospital of Philadelphia (US)
- California State University, Fullerton (US)
- University of Southern California (US)
- University of Pennsylvania (US)
- Corresponding author
- Wang, Kai
- Venue
- Genes
- Publisher
- MDPI AG
- Published
- 2019-03-21
- Volume
- 10
- Issue
- 3
- Pages
- 240
- ISSN
- 2073-4425 (ISSN-L)
- PMC
- PMC6471789
- OpenAlex
- W2922815614
- Cited by
- 102
- References
- 39
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 26 · 2023: 23 · 2022: 5 · 2021: 15 · 2020: 13 · 2019: 1
- FWCI
- 2.81
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Genes (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- Children's Hospital of Philadelphia (NA)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- cancer prognosis, multi-omics data, group lasso regularization, Cox proportional hazards model, survival prediction, gene expression, DNA methylation, protein expression, copy number alterations, somatic mutations, clinical features, glioblastoma, clear cell renal cell carcinoma, bladder urothelial carcinoma, TCGA
- MeSH terms
- Deep Learning, Humans, Neoplasms, Prognosis, DNA Methylation, Computational Biology, Gene Expression Profiling, Genomics
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