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<p>The ability to accurately measure aberrant DNA methylation levels is integral to the understanding of DNA methylation biology. It is well-established that in cancer, the largest, and thus, most biologically important absolute gains of DNA methylation levels occur at CpG sites with low native levels while the largest losses occur at CpG sites with high native levels. Conventional wisdom assumes that the observed association between the degree of the alterations and the native levels are largely due to the limitations of change within the DNA methylation scale. Here, we present evidence that this association is largely caused by alterations occurring as a global rate of change relative to the native level. We show that DNA methylation alterations can be accurately compared by calculating the rate of change relative to the native level. Most importantly, this approach enables the identification of more biologically significant DNA methylation alterations.</p>

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An unbiased approach to measure aberrant DNA methylation alterations

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月27日

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  • Bradley M. Downs,
  • Jiumei Hu,
  • Joon Soo Park,
  • Hanran Lei,
  • Tza-Huei Wang,
  • Thomas R. Pisanic II,
  • Kuangwen Hsieh,
  • Tra My Hoang

摘要

The ability to accurately measure aberrant DNA methylation levels is integral to the understanding of DNA methylation biology. It is well-established that in cancer, the largest, and thus, most biologically important absolute gains of DNA methylation levels occur at CpG sites with low native levels while the largest losses occur at CpG sites with high native levels. Conventional wisdom assumes that the observed association between the degree of the alterations and the native levels are largely due to the limitations of change within the DNA methylation scale. Here, we present evidence that this association is largely caused by alterations occurring as a global rate of change relative to the native level. We show that DNA methylation alterations can be accurately compared by calculating the rate of change relative to the native level. Most importantly, this approach enables the identification of more biologically significant DNA methylation alterations.