4.7 Article

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

期刊

BRIEFINGS IN BIOINFORMATICS
卷 23, 期 4, 页码 -

出版社

OXFORD UNIV PRESS
DOI: 10.1093/bib/bbac278

关键词

cell-free DNA; ribosomal DNA; cancer detection; DNA methylation; mapping pipeline

资金

  1. National Key R&D Program of China [2020YFA0906900]
  2. National Natural Science Foundation of China [62050152, 61773230, 61721003, 81902495]
  3. Medical Big Data and AIR&D Project of General Hospita l [2019 MBD-025]
  4. Science, Technology and Innovation Commission of Shenzhen Municipality [KCXFZ202002011006448]
  5. Project of Tsinghua Fuzhou Institute for Data Technology [TFIDT2021006]

向作者/读者索取更多资源

This study designed a specific mapping strategy to investigate the methylation patterns of ribosomal DNA (rDNA) and found that rDNA methylation profiles can serve as biomarkers for cancer detection with good performances.
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 (<1x). Taken together, these findings broaden our understanding of rDNA regulation and suggest the potential utility of rDNA methylation features as disease biomarkers.

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