Epigenetic Signatures in Ovarian Cancer to Determine Potential Diagnostic/Prognostic Biomarkers

Abstract

Objective: Identification of methylation patterns in cell-free DNA (cfDNA) provides a non-invasive methodology for discovering critical biomarkers that facilitate detection and prognostic evaluation of ovarian cancer (OC). This study explored the epigenetic landscape of OC by examining the DNA methylation patterns of cfDNA. Materials and Methods: Plasma samples from 5 OC patients and 5 healthy blood donors (HBDs) were processed for cfDNA isolation and methylated DNA immunoprecipitation, followed by next-generation sequencing and bioinformatics analysis to identify differentially methylated regions (DMRs) and genes (DMGs). Integration with The Cancer Genome Atlas (TCGA) data identified differentially expressed genes (DEGs) for functional analysis. Results: The analysis revealed significant alterations in DNA methylation patterns, with 62 hypermethylated and 2 hypomethylated DMRs in OC compared with HBDs. Hierarchical clustering revealed distinct methylation patterns between OC and HBDs. Integrative analysis identified 18 genes with overlapping methylation and expression changes in OC and a negative correlation between methylation and expression levels (p<0.05). Ten genes exhibited a hypermethylation-downregulation pattern, indicating a suppressive role, whereas eight showed hypermethylation-upregulation. Survival analysis of OC data from TCGA highlighted B3GNT3 (p=0.04) and LRP1B (p=0.053) as promising prognostic markers. Conclusion: Our study revealed an intricate relationship between DNA methylation alterations and gene expression dysregulation in ovarian cancer. We found that hypermethylation of B3GNT3 was correlated with its upregulation and poor survival outcomes, whereas hypermethylation of LRP1B pointed to its role as a tumor suppressor gene.

Keywords

• Epithelial ovarian cancer
• methylation
• epigenomics
• liquid biopsy
• cfDNA

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