Evaluating methylation of human ribosomal DNA at each CpG site reveals its utility for cancer detection using cell-free DNA

Author:

Zhang Xianglin1,Jia Xiaodong2,Zhong Bixi1,Wei Lei1,Li Jiaqi1ORCID,Zhang Wei1ORCID,Fang Huan1,Li Yanda1,Lu Yinying345,Wang Xiaowo1ORCID

Affiliation:

1. Ministry of Education Key Laboratory of Bioinformatics; Center for Synthetic and Systems Biology; Beijing National Research Center for Information Science and Technology; Department of Automation, Tsinghua University , Beijing 100084, China

2. Senior Department of Oncology, Fifth Medical Center of PLA General Hospital , Beijing 100039, China

3. Comprehensive Liver Cancer Center, Fifth Medical Center of PLA General Hospital , Beijing 100039, China

4. Center for Synthetic and Systems Biology, Tsinghua University , Beijing, 100084, China

5. Guangdong Key Laboratory of Epigenetics, College of Life Sciences and Oceanography, Shenzhen University , Shenzhen, Guangdong, 518055, China

Abstract

Abstract Ribosomal deoxyribonucleic acid (DNA) (rDNA) repeats are tandemly located on five acrocentric chromosomes with up to hundreds of copies in the human genome. DNA methylation, the most well-studied epigenetic mechanism, has been characterized for most genomic regions across various biological contexts. However, rDNA methylation patterns remain largely unexplored due to the repetitive structure. In this study, we designed a specific mapping strategy to investigate rDNA methylation patterns at each CpG site across various physiological and pathological processes. We found that CpG sites on rDNA could be categorized into two types. One is within or adjacent to transcribed regions; the other is distal to transcribed regions. The former shows highly variable methylation levels across samples, while the latter shows stable high methylation levels in normal tissues but severe hypomethylation in tumors. We further showed that rDNA methylation profiles in plasma cell-free DNA could be used as a biomarker for cancer detection. It shows good performances on public datasets, including colorectal cancer [area under the curve (AUC) = 0.85], lung cancer (AUC = 0.84), hepatocellular carcinoma (AUC = 0.91) and in-house generated hepatocellular carcinoma dataset (AUC = 0.96) even at low genome coverage (<1×). Taken together, these findings broaden our understanding of rDNA regulation and suggest the potential utility of rDNA methylation features as disease biomarkers.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Medical Big Data and AI R&D Project of General Hospital

Science, Technology and Innovation Commission of Shenzhen Municipality

Project of Tsinghua Fuzhou Institute for Data Technology

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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