Next Article in Journal
Conservation Genetic Assessment of Savannah Elephants (Loxodonta africana) in the Greater Kruger Biosphere, South Africa
Previous Article in Journal
Ectopic Expression of Multiple Chrysanthemum (Chrysanthemum × morifolium) R2R3-MYB Transcription Factor Genes Regulates Anthocyanin Accumulation in Tobacco
Previous Article in Special Issue
A Portal to Visualize Transcriptome Profiles in Mouse Models of Neurological Disorders
Open AccessArticle

DNA Methylation Markers for Pan-Cancer Prediction by Deep Learning

by Biao Liu 1,†, Yulu Liu 1,†, Xingxin Pan 1, Mengyao Li 2, Shuang Yang 1,* and Shuai Cheng Li 3,*
1
BGI Education Center, University of Chinese Academy of Sciences, Shenzhen 518083, China
2
Research and Development Department, Shenzhen Byoryn Technology Co.,Ltd, Shenzhen 518000, China
3
Department of Computer Science, City University of Hong Kong, Kowloon 999077, Hong Kong
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Genes 2019, 10(10), 778; https://doi.org/10.3390/genes10100778
Received: 27 June 2019 / Revised: 21 September 2019 / Accepted: 30 September 2019 / Published: 4 October 2019
For cancer diagnosis, many DNA methylation markers have been identified. However, few studies have tried to identify DNA methylation markers to diagnose diverse cancer types simultaneously, i.e., pan-cancers. In this study, we tried to identify DNA methylation markers to differentiate cancer samples from the respective normal samples in pan-cancers. We collected whole genome methylation data of 27 cancer types containing 10,140 cancer samples and 3386 normal samples, and divided all samples into five data sets, including one training data set, one validation data set and three test data sets. We applied machine learning to identify DNA methylation markers, and specifically, we constructed diagnostic prediction models by deep learning. We identified two categories of markers: 12 CpG markers and 13 promoter markers. Three of 12 CpG markers and four of 13 promoter markers locate at cancer-related genes. With the CpG markers, our model achieved an average sensitivity and specificity on test data sets as 92.8% and 90.1%, respectively. For promoter markers, the average sensitivity and specificity on test data sets were 89.8% and 81.1%, respectively. Furthermore, in cell-free DNA methylation data of 163 prostate cancer samples, the CpG markers achieved the sensitivity as 100%, and the promoter markers achieved 92%. For both marker types, the specificity of normal whole blood was 100%. To conclude, we identified methylation markers to diagnose pan-cancers, which might be applied to liquid biopsy of cancers.
Keywords: biomarker, methylation, pan-cancer, deep learning, CpG, promoter biomarker, methylation, pan-cancer, deep learning, CpG, promoter
MDPI and ACS Style

Liu, B.; Liu, Y.; Pan, X.; Li, M.; Yang, S.; Li, S.C. DNA Methylation Markers for Pan-Cancer Prediction by Deep Learning. Genes 2019, 10, 778.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop