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Proc Natl Acad Sci U S A. 2017 Jul 11;114(28):7414-7419. doi: 10.1073/pnas.1703577114. Epub 2017 Jun 26.

DNA methylation markers for diagnosis and prognosis of common cancers.

Author information

1
Department of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China; haoxkg@fmmu.edu.cn mkarin@ucsd.edu xurh@sysucc.org.cn kang.zhang@gmail.com.
2
State Key Laboratory of Oncology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China.
3
Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA 92093.
4
Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai General Hospital, Shanghai 200080, China.
5
Department of Clinical Laboratory Medicine, Xijing Hospital, Fourth Military Medical University, Xi'an 710032, China.
6
Shanghai Center for Plant Stress Biology, Shanghai Institute for Biological Sciences, Chinese Academy of Sciences, Shanghai 210602, China.
7
Guangzhou Youze Biological Pharmaceutical Technology Company Ltd., Guangzhou 510005, China.
8
Department of Pharmacology, University of California, San Diego, La Jolla, CA 92328.
9
Department of Pharmacology, University of California, San Diego, La Jolla, CA 92328; haoxkg@fmmu.edu.cn mkarin@ucsd.edu xurh@sysucc.org.cn kang.zhang@gmail.com.
10
State Key Laboratory of Oncology, Sun Yat-sen University Cancer Center, Guangzhou 510060, China; haoxkg@fmmu.edu.cn mkarin@ucsd.edu xurh@sysucc.org.cn kang.zhang@gmail.com.
11
Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA 92093; haoxkg@fmmu.edu.cn mkarin@ucsd.edu xurh@sysucc.org.cn kang.zhang@gmail.com.
12
Veterans Administration Healthcare System, San Diego, CA 92093.

Abstract

The ability to identify a specific cancer using minimally invasive biopsy holds great promise for improving the diagnosis, treatment selection, and prediction of prognosis in cancer. Using whole-genome methylation data from The Cancer Genome Atlas (TCGA) and machine learning methods, we evaluated the utility of DNA methylation for differentiating tumor tissue and normal tissue for four common cancers (breast, colon, liver, and lung). We identified cancer markers in a training cohort of 1,619 tumor samples and 173 matched adjacent normal tissue samples. We replicated our findings in a separate TCGA cohort of 791 tumor samples and 93 matched adjacent normal tissue samples, as well as an independent Chinese cohort of 394 tumor samples and 324 matched adjacent normal tissue samples. The DNA methylation analysis could predict cancer versus normal tissue with more than 95% accuracy in these three cohorts, demonstrating accuracy comparable to typical diagnostic methods. This analysis also correctly identified 29 of 30 colorectal cancer metastases to the liver and 32 of 34 colorectal cancer metastases to the lung. We also found that methylation patterns can predict prognosis and survival. We correlated differential methylation of CpG sites predictive of cancer with expression of associated genes known to be important in cancer biology, showing decreased expression with increased methylation, as expected. We verified gene expression profiles in a mouse model of hepatocellular carcinoma. Taken together, these findings demonstrate the utility of methylation biomarkers for the molecular characterization of cancer, with implications for diagnosis and prognosis.

KEYWORDS:

DNA methylation; cancer diagnosis; cancer prognosis; gene expression; survival analysis

PMID:
28652331
PMCID:
PMC5514741
DOI:
10.1073/pnas.1703577114
[Indexed for MEDLINE]
Free PMC Article

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