4.6 Article

Expression of 5-methylcytosine regulators is highly associated with the clinical phenotypes of prostate cancer and DNMTs expression predicts biochemical recurrence

Journal

CANCER MEDICINE
Volume 10, Issue 16, Pages 5681-5695

Publisher

WILEY
DOI: 10.1002/cam4.4108

Keywords

biochemical recurrence; DNA methyltransferase; immune infiltration; prostate cancer

Categories

Funding

  1. National Science and Technology Major Project of China [2018ZX10302205]
  2. National Key R&D Program of China [2016YFC1303401]

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This study identified global coexpression and comethylation modules in PCa, revealing the important roles of DNA methyltransferases in pathologic phenotypes and immune infiltration patterns. A two-factor predictive model using DNMT3B and DNMT1 expression features successfully predicted the biochemical recurrence status of patients with PCa.
In patients with prostate cancer (PCa), there is a high rate of overdiagnosis and frequent overtreatment. Therefore, there is an urgent need for more accurate prediction of biochemical recurrence (BCR). DNA methylation regulation patterns play crucial roles in tumorigenicity, progression, and treatment efficacy in PCa. However, the global relationship between epigenetic alterations, changes in mRNA levels, and pathologic phenotypes of PCa remain largely undefined. Here, we conducted a systematic analysis to identify global coexpression and comethylation modules in PCa. We identified coregulated methylation and expression modules and the relationships between epigenetic modifications, tumor progression, and the corresponding immune microenvironment in PCa. Our results show that DNA methyltransferases (DNMTs) are strongly associated with pathologic phenotypes and immune infiltration patterns in PCa. We built a two-factor predictive model using the expression features of DNMT3B and DNMT1. The model was used to predict the BCR status of patients with PCa and achieved area under the receiver operating characteristic curve values of 0.70 and 0.88 in the training and independent testing datasets, respectively.

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