  DNA methylation is a well-studied genetic modification that regulates gene
transcription of Eukaryotes. Its alternations have been recognized as a
significant component of cancer development. In this study, we use the DNA
methylation 450k data from The Cancer Genome Atlas to evaluate the efficacy of
DNA methylation data on cancer classification for 30 cancer types. We propose a
new method for gene selection in high dimensional data(over 450 thousand).
Variance filtering is first introduced for dimension reduction and Recursive
feature elimination (RFE) is then used for feature selection. We address the
problem of selecting a small subsets of genes from large number of methylated
sites, and our parsimonious model is demonstrated to be efficient, achieving an
accuracy over 91%, outperforming other studies which use DNA micro-arrays and
RNA-seq Data . The performance of 20 models, which are based on 4 estimators
(Random Forest, Decision Tree, Extra Tree and Support Vector Machine) and 5
classifiers (k-Nearest Neighbours, Support Vector Machine, XGboost, Light GBM
and Multi-Layer Perceptron), is compared and robustness of the RFE algorithm is
examined. Results suggest that the combined model of extra tree plus catboost
classifier offers the best performance in cancer identification, with an
overall validation accuracy of 91% , 92.3%, 93.3% and 93.5% for 20, 30, 40 and
50 features respectively. The biological functions in cancer development of 50
selected genes is also explored through enrichment analysis and the results
show that 12 out of 16 of our top features have already been identified to be
specific with cancer and we also propose some more genes to be tested for
future studies. Therefore, our method may be utilzed as an auxiliary diagnostic
method to determine the actual clinicopathological status of a specific cancer.
