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Frontiers in bioengineering and biotechnology
2019;7 388.

CancerClock: A DNA Methylation Age Predictor to Identify and Characterize Aging Clock in Pan-Cancer.

Tongtong Zhu, Yue Gao, Junwei Wang, Xin Li, Shipeng Shang, Yanxia Wang, Shuang Guo, Hanxiao Zhou, Hongjia Liu, Dailin Sun, Hong Chen, Li Wang, Shangwei Ning
Author Information
  1. Tongtong Zhu: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  2. Yue Gao: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  3. Junwei Wang: Department of Respiratory Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
  4. Xin Li: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  5. Shipeng Shang: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  6. Yanxia Wang: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  7. Shuang Guo: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  8. Hanxiao Zhou: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  9. Hongjia Liu: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  10. Dailin Sun: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  11. Hong Chen: Department of Respiratory Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, China.
  12. Li Wang: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
  13. Shangwei Ning: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
PMID: 31867319 DOI: 10.3389/fbioe.2019.00388

Abstract

Many biological indicators related to chronological age have been proposed. Recent studies found that epigenetic clock or DNA methylation age is highly correlated with chronological age. In particular, a significant difference between DNA methylation age (m-age) and chronological age was observed in cancers. However, the prediction and characterization of m-age in pan-cancer remains an explored area. In this study, 1,631 age-related methylation sites in normal tissues were discovered and analyzed. A comprehensive computational model named CancerClock was constructed to predict the m-age for normal samples based on methylation levels of the extracted methylation sites. LASSO linear regression model was used to screen and train the CancerClock model in normal tissues. The accuracy of CancerClock has proved to be 81%, and the correlation value between chronological age and m-age was 0.939 ( < 0.01). Next, CancerClock was used to evaluate the difference between m-age and chronological age for 33 cancer types from TCGA. There were significant differences between predicted m-age and chronological age in large number of cancer samples. These cancer samples were defined as "age-related cancer samples" and they have some differential methylation sites. The differences between predicted m-age and chronological age may contribute to cancer development. Some of these differential methylation sites were associated with cancer survival. CancerClock provided assistance in estimating the m-age in normal and cancer samples. The changes between m-age and chronological age may improve the diagnosis and prognosis of cancers.

Keywords

LASSO chronological age methylation age pan-cancer survival

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