Breast cancer is the most frequent cancer in women and the second most frequent overall after lung cancer.
Although the 5-year survival rate of breast cancer is relatively high, recurrence is also common which often
involves metastasis with its consequent threat for patients. DNA methylation-derived databases have become an
interesting primary source for supervised knowledge extraction regarding breast cancer. Unfortunately, the study
of DNA methylation involves the processing of hundreds of thousands of features for every patient. DNA
methylation is featured by High Dimension Low Sample Size which has shown well-known issues regarding
feature selection and generation. Autoencoders (AEs) appear as a specific technique for conducting nonlinear
feature fusion. Our main objective in this work is to design a procedure to summarize DNA methylation by taking
advantage of AEs. Our proposal is able to generate new features from the values of CpG sites of patients with and
without recurrence. Then, a limited set of relevant genes to characterize breast cancer recurrence is proposed by
the application of survival analysis and a pondered ranking of genes according to the distribution of their CpG
sites. To test our proposal we have selected a dataset from The Cancer Genome Atlas data portal and an AE with a
single-hidden layer. The literature and enrichment analysis (based on genomic context and functional annota tion) conducted regarding the genes obtained with our experiment confirmed that all of these genes were related
to breast cancer recurrence.Ministerio de Economía y Competitividad TIN2014-55894-C2-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-