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<p>DNA methylation plays a crucial role in the development and progression of cancer and has been utilized for subtyping various tumors. This study focused on classifying epithelial ovarian cancer (EOC) based on DNA methylation and characterizing the subtypes through an integrated analysis of genomic, transcriptomic, and clinical data. We performed genome-wide DNA methylation profiling on 137 EOC tumor tissues using Infinium MethylationEPIC array and four methylation subtypes (MS1-MS4) were identified by non-negative matrix factorization (NMF) approach, showing significant differences in prognosis (<i>P</i> = 2.413 × 10⁻⁹). The MS1 group showed the best prognosis and the most favorable response to paclitaxel in combination with platinum-based chemotherapy. MS2 exhibited a gene expression pattern of relatively high immune cell infiltration and MS3 had a gene expression pattern associated with metabolic related pathway with a moderate prognosis. In contrast, MS4 had the poorest prognosis and was marked by the highest methylation levels among the four subtypes. A four-differential methylation position (DMP) signature was constructed for prognosis prediction and nomogram was also developed for enhancing clinical utility. Together, this study identified a novel molecular subtype for EOC, elucidating the heterogeneity of EOC from an epigenetic perspective and providing a new strategy for personalized treatment options for EOC patients.</p>

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Identification of DNA methylation based prognostic subtype and signature in epithelial ovarian cancer

  • 期刊论文

  • 发表日期：

    2025年12月27日

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  • Jingjing Zhou,
  • Yuxin Chen,
  • Wei Liu,
  • Chao Wang,
  • Lei Liu,
  • Wei Geng,
  • Linqing Chai,
  • Dong Yang,
  • Hongji Dai,
  • Haixin Li,
  • Yanrui Zhao,
  • Kexin Chen,
  • Hong Zheng,
  • Lian Li

摘要

DNA methylation plays a crucial role in the development and progression of cancer and has been utilized for subtyping various tumors. This study focused on classifying epithelial ovarian cancer (EOC) based on DNA methylation and characterizing the subtypes through an integrated analysis of genomic, transcriptomic, and clinical data. We performed genome-wide DNA methylation profiling on 137 EOC tumor tissues using Infinium MethylationEPIC array and four methylation subtypes (MS1-MS4) were identified by non-negative matrix factorization (NMF) approach, showing significant differences in prognosis (P = 2.413 × 10⁻⁹). The MS1 group showed the best prognosis and the most favorable response to paclitaxel in combination with platinum-based chemotherapy. MS2 exhibited a gene expression pattern of relatively high immune cell infiltration and MS3 had a gene expression pattern associated with metabolic related pathway with a moderate prognosis. In contrast, MS4 had the poorest prognosis and was marked by the highest methylation levels among the four subtypes. A four-differential methylation position (DMP) signature was constructed for prognosis prediction and nomogram was also developed for enhancing clinical utility. Together, this study identified a novel molecular subtype for EOC, elucidating the heterogeneity of EOC from an epigenetic perspective and providing a new strategy for personalized treatment options for EOC patients.