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DNA methylation and transcription factor-driven immune subtypes in ovarian cancer

Jingshu Hu1, Mu Su1, Zhijun Qin1

  • 1Department of Gynecologic Oncology, Harbin Medical University Cancer Hospital Harbin, Heilongjiang, 150000, China.

Discover Oncology
|August 28, 2025
PubMed

Insights

Ovarian cancer (OC) can be classified into two immune subtypes based on DNA methylation and transcription factors. This classification helps predict patient prognosis and identify potential therapeutic targets for personalized treatment.

Area of Science:

  • Oncology
  • Immunology
  • Genomics
  • Bioinformatics

Background:

  • Ovarian cancer (OC) is a leading cause of gynecological cancer mortality.
  • Limited efficacy of immune checkpoint blockade (ICB) in OC necessitates understanding the tumor immune microenvironment.
  • DNA methylation and transcription factors are implicated in immunotherapy response.

Purpose of the Study:

  • To classify ovarian cancer into distinct immune subtypes using DNA methylation and transcription factor data.
  • To identify prognostic biomarkers and potential therapeutic targets for OC.

Main Methods:

  • Comprehensive bioinformatics analysis of The Cancer Genome Atlas (TCGA) data.
  • Identification of differentially methylated genes (DMGs) associated with transcription factors.
  • Development and validation of a prognostic prediction model using LASSO and Cox regression.
  • Drug sensitivity analysis and immunohistochemical validation.

Main Results:

  • Ovarian cancer was categorized into two immune subtypes (C1 and C2) based on methylation and transcription factor profiles.
  • Subtype C1 showed higher immune infiltration and better prognosis ('hot' tumors), while C2 had lower infiltration and poorer prognosis ('cold' tumors).
  • A four-gene prognostic model (KRT81, PAPPA2, FGF10, FMO2) accurately stratified patients into high- and low-risk groups.
  • KRT81 was confirmed as a potential prognostic marker via immunohistochemistry.

Conclusions:

  • The study provides a novel classification of OC into immune subtypes, enhancing understanding of the tumor immune microenvironment.
  • Identified key genes and a prognostic model offer potential for improved patient stratification and personalized treatment strategies.
  • Findings may pave the way for novel therapeutic interventions to improve ovarian cancer outcomes.
Keywords:
DNA methylationImmune subtypesOvarian cancerPrecision medicinePrognostic modelTranscription factors

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