Review finds DNA methylation promising, but not ready for ovarian cancer screening

A 2026 review concludes that DNA methylation biomarkers can distinguish ovarian cancer in several blood-based studies, but no assay has yet shown the combined stage I–II sensitivity, specificity, histological coverage and prospective validation required for population screening. The review searched PubMed/MEDLINE, Web of Science and Scopus for studies published from January 2020 through September 2025, supplementing them with selected earlier studies relevant to biomarker biology or methodology. Because populations, biomarkers, platforms, thresholds and outcome measures varied substantially, the authors synthesized the evidence qualitatively rather than conducting a quantitative meta-analysis.

Ovarian cancer accounted for 324,398 new cases and 206,839 deaths globally in GLOBOCAN 2022, a mortality burden disproportionately high relative to incidence. Outcomes are more favorable when disease is detected early, but large screening efforts combining CA125 and transvaginal ultrasonography have not demonstrated sufficient survival benefit to support population screening. CA125 also has limited sensitivity in early-stage disease and can be elevated in benign and non-ovarian malignant conditions. Modelling cited by the review indicates that an early-stage screening test would need sensitivity above 75% and specificity of at least 99.6% to achieve a positive predictive value of 10% or greater.

DNA methylation offers a possible signal because aberrant methylation may arise early in tumourigenesis and can be measured in circulating cell-free DNA. However, plasma cell-free DNA contains material from many tissues, and only a fraction may be tumour-derived. Delays in blood processing can increase contamination with high-molecular-weight DNA, while methylation conversion, extraction and threshold selection can further affect measured signal. These issues are particularly consequential when early tumours release relatively few tumour-derived molecules.

Single-gene studies produced variable results. RASSF1A sensitivities of 25.4%, 37.7% and 85.7% were reported across three blood-based studies. OPCML showed the strongest overall performance among commonly assessed single-gene markers, with reported sensitivities of approximately 80.0% to 97.8%, but it has not been reproduced in large independent prospective cohorts. In a North Indian case-control study, serum HOXA9 and HIC1 methylation had sensitivities of 62.2% and 71.1%, respectively, at 100% specificity, although the stage I–II subset contained only 10 patients and controls were healthy rather than women with benign pelvic disease.

Multi-gene panels often improved discrimination within their source cohorts. A HOXA9–HIC1 combination tested in 45 epithelial ovarian cancer cases and 25 healthy controls achieved 88.9% sensitivity, 100% specificity and an area under the curve of 0.95, and was positive in all 10 stage I–II serum cases. Separately, an 11-marker random-forest model achieved 79% sensitivity at 96% specificity in 91 ovarian cancer cases and 91 controls and identified all five stage I–II high-grade serous cancers included in its plasma cohort. These small early-stage denominators and retrospective case-control designs do not establish screening performance.

Pre-diagnostic testing exposed a substantial gap between diagnostic discrimination and early detection. The three-region WID-cfOC score achieved 97.6% specificity and 66.7% sensitivity across 27 cancer cases in a diagnostic cohort that included women with benign pelvic pathology. Applied to prediagnostic samples from 29 women who subsequently developed ovarian cancer and 29 risk-matched controls, however, sensitivity was 27.3% for high-risk cancers with relatively lower genomic-DNA contamination and 33.3% in samples collected within one year of diagnosis, despite 100% specificity. Neither methylation nor CA125 detected stage I cancers in the diagnostic cohort, and combining the tests did not improve sensitivity beyond CA125 alone in the pre-diagnostic subset.

Machine-learning classifiers produced encouraging but threshold-dependent results. A transformer-based MethylBERT model evaluated 493 cfDNA methylation markers in 754 ovarian cancer cases, including 205 early-stage cases, and 1,118 healthy women. In its held-out internal validation set, the model achieved 89.24% sensitivity at 94.39% specificity and 79.45% sensitivity among 73 early-stage cases. Raising specificity from about 95% to 99.2% reduced early-stage sensitivity to 63.01%. Combining the classifier with CA125 increased early-stage sensitivity from 82.51% to 89.62% but reduced specificity from 97.36% to 93.55%, illustrating the trade-off between sensitivity and false-positive classification.

Validation quality remains a central limitation. A tissue-based high-grade serous classifier developed with more than 850,000 probes achieved an area under the curve of 0.84 (95% CI 0.76–0.93) in external validation after reduction to nine probes, compared with 0.98 for a much larger model. Its tissue samples, predominantly advanced-stage disease and a largely homogeneous population limit extrapolation to blood-based early detection. Across the wider literature, retrospective case-control designs, small FIGO stage I–II subsets, predominance of serous disease and healthy-control groups limit generalisability. Histotype-specific MethylBERT estimates also rested on only 43 serous, 11 endometrioid, eight mucinous and six clear-cell early-stage cases.

Assay chemistry and pre-analytical procedures add further uncertainty. Targeted PCR methods are comparatively practical for predefined loci, but bisulfite conversion can cause DNA fragmentation and loss, with reported recovery rates ranging from approximately 22% to 66% across conversion kits. Enzymatic and enrichment methods can reduce some conversion losses and work with low DNA input, but they have not established ovarian cancer screening performance. In 29 reviewed blood-based studies, only six reported the interval between collection and processing, only four used double centrifugation, and only 11 used extraction kits designed specifically for cell-free DNA. The review’s bounded conclusion is that methylation assays remain promising research components rather than clinically validated population-screening tests.

Subject of Research: Evaluation of DNA methylation biomarkers for early detection of ovarian cancer

Article Title: DNA methylation biomarkers for early detection of ovarian cancer

Article References: Leow, J. Y.-S., Ng, N. C.-S., Wong, R. S.-Y., Ming, L. C., Yap, T. L., & Goh, B. H. (2026). DNA methylation biomarkers for early detection of ovarian cancer. Molecular Biology Reports, 53(1), Article 1562. https://doi.org/10.1007/s11033-026-12750-6

Image Credits: AI Generated

DOI: 10.1007/s11033-026-12750-6

Keywords: ovarian cancer, DNA methylation, cell-free DNA, early detection, liquid biopsy, biomarkers, machine learning, CA125, diagnostic accuracy, methylation, early, detection