DNA methylation-based profiling reveals distinct clusters with survival heterogeneity in high-grade serous ovarian cancer

Clin Epigenetics. 2021 Oct 13;13(1):190. doi: 10.1186/s13148-021-01178-3.

Abstract

High-grade serous ovarian cancer (HGSOC) is the most common type of epigenetically heterogeneous ovarian cancer. Methylation typing has previously been used in many tumour types but not in HGSOC. Methylation typing in HGSOC may promote the development of personalized care. The present study used DNA methylation data from The Cancer Genome Atlas database and identified four unique methylation subtypes of HGSOC. With the poorest prognosis and high frequency of residual tumours, cluster 4 featured hypermethylation of a panel of genes, which indicates that demethylation agents may be tested in this group and that neoadjuvant chemotherapy may be used to reduce the possibility of residual lesions. Cluster 1 and cluster 2 were significantly associated with metastasis genes and metabolic disorders, respectively. Two feature CpG sites, cg24673765 and cg25574024, were obtained through Cox proportional hazards model analysis of the CpG sites. Based on the methylation level of the two CpG sites, the samples were classified into high- and low-risk groups to identify the prognostic information. Similar results were obtained in the validation set. Taken together, these results explain the epigenetic heterogeneity of HGSOC and provide guidance to clinicians for the prognosis of HGSOC based on DNA methylation sites.

Keywords: DNA profiling; High-grade serous ovarian cancer; Methylation subtypes; Ovarian cancer; Prognosis.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Cystadenocarcinoma, Serous / complications
  • Cystadenocarcinoma, Serous / genetics*
  • Cystadenocarcinoma, Serous / mortality
  • DNA Methylation / genetics*
  • Female
  • Humans
  • Middle Aged
  • Ovarian Neoplasms / complications
  • Ovarian Neoplasms / genetics*
  • Ovarian Neoplasms / mortality
  • Prognosis
  • Proportional Hazards Models