Abstract
Epigenetic alterations have an important role in the development of several types of cancer. Epigenetic studies generate a large amount of data, which makes it essential to develop novel models capable of dealing with large-scale data. In this work, we propose a deep embedded refined clustering method for breast cancer differentiation based on DNA methylation. In concrete, the deep learning system presented here uses the levels of CpG island methylation between 0 and 1. The proposed approach is composed of two main stages. The first stage consists in the dimensionality reduction of the methylation data based on an autoencoder. The second stage is a clustering algorithm based on the soft assignment of the latent space provided by the autoencoder. The whole method is optimized through a weighted loss function composed of two terms: reconstruction and classification terms. To the best of the authors’ knowledge, no previous studies have focused on the dimensionality reduction algorithms linked to classification trained end-to-end for DNA methylation analysis. The proposed method achieves an unsupervised clustering accuracy of 0.9927 and an error rate (%) of 0.73 on 137 breast tissue samples. After a second test of the deep-learning-based method using a different methylation database, an accuracy of 0.9343 and an error rate (%) of 6.57 on 45 breast tissue samples are obtained. Based on these results, the proposed algorithm outperforms other state-of-the-art methods evaluated under the same conditions for breast cancer classification based on DNA methylation data.
Highlights
Epigenetic mechanisms are crucial for the normal development and maintenance of tissue-specific gene expression profiles in mammals
We propose a deep embedded refined clustering method for breast cancer differentiation based on DNA methylation
We detail a comparison between the latent space obtained using the conventional and the variational autoencoder and the unsupervised classification results after applying the
Summary
Epigenetic mechanisms are crucial for the normal development and maintenance of tissue-specific gene expression profiles in mammals. DNAm takes place in cytosines that precede guanines, known as CpG dinucleotides [8]. CpG sites are not randomly distributed throughout the genome, but there are CpG-rich areas known as CpG islands often located in the gene promoting regions. CpG islands are usually largely unmethylated in normal cells. The methylation of these CpG sites silences the promoter activity and correlates negatively with the gene expression. The methylation of the promoter regions in some vital genes, such as tumor suppressor genes, and their inactivation, has been firmly established as one of the most common mechanisms for cancer development [6, 19]. Because the methylation patterns can be observed in the early stages of cancer [24], DNA methylation analysis
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