Journal
FRONTIERS IN GENETICS
Volume 9, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fgene.2018.00477
Keywords
deep learning; high-risk neuroblastoma; multi-omics data integration; MYCN amplification; machine learning
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Funding
- China Human Proteome Project [2014DFB30010, 2014DFB30030]
- National Key Research and Development Program of China [2016YFC0902100]
- National Natural Science Foundation of China [31671377, 81472369, 81502144]
- Shanghai 111 Project [B14019]
- Clinical Application Research Funds of Capital Beijing [Z171100001017051]
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High-risk neuroblastoma is a very aggressive disease, with excessive tumor growth and poor outcomes. A proper stratification of the high-risk patients by prognostic outcome is important for treatment. However, there is still a lack of survival stratification for the high-risk neuroblastoma. To fill the gap, we adopt a deep learning algorithm, Autoencoder, to integrate multi-omics data, and combine it with K-means clustering to identify two subtypes with significant survival differences. By comparing the Autoencoder with PCA, iCluster, and DGscore about the classification based on multi-omics data integration, Autoencoder-based classification outperforms the alternative approaches. Furthermore, we also validated the classification in two independent datasets by training machine-learning classification models, and confirmed its robustness. Functional analysis revealed that MYCN amplification was more frequently occurred in the ultra-high-risk subtype, in accordance with the overexpression of MYC/MYCN targets in this subtype. In summary, prognostic subtypes identified by deep learning-based multiomics integration could not only improve our understanding of molecular mechanism, but also help the clinicians make decisions.
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