Integrative analysis of epigenetic and transcriptional interrelations identifies histotype-specific biomarkers in early-stage ovarian carcinoma.

Abstract

Background: Epithelial ovarian cancer (EOC) is a deadly and heterogenous disease comprising five major histotypes: clear cell carcinoma (CCC), endometrioid carcinoma (EC), low- and high-grade serous carcinoma (LGSC, HGSC), and mucinous carcinoma (MC). Despite this heterogeneity, EOC is often treated as a homogenous disease, and reliable screening tests are lacking. Although progress has been made, there is a pressing need for biomarkers to refine patient stratification, guide treatment, and improve outcomes. Here, we elucidated the relationship between DNA methylation and gene expression patterns in EOC to identify histotype-specific biomarkers.

Methods: Differential DNA methylation and gene expression analyses were performed for 86 early-stage EOC samples after histopathological reclassification stratified by histotype. The correlation between DNA methylation and gene expression was examined, and histotype-specific biomarkers were identified. Hierarchical clustering and predictive machine learning modeling were employed to assess the performance of the histotype-specific biomarkers using four external cohorts.

Results: EOC histotypes exhibited distinct epigenetic, transcriptional, and functional profiles, with candidate histotype-specific biomarkers such as CTSE and VCAN effectively distinguishing CCC, HGSC, and MC on the transcriptional level. Gene expression for the candidate biomarkers was found to be reproducible across external cohorts, with histotype-specific differences remaining homogenous.

Conclusions: This study identified promising histotype-specific biomarkers for EOC using integrative transcriptomic and epigenomic analysis. Furthermore, these findings indicate that additional stratification or potential reclassification of the EC histotype is warranted in future studies.