Methylation-based test for diagnosis of benign and malignant melanocytoma.

BACKGROUND

The accurate diagnosis of melanoma significantly improves patient survival rates. Distinguishing melanomas from naevi purely by morphology can be challenging when neoplastic cells are confined to the epidermis or lack marked nuclear pleomorphism.

OBJECTIVES

To investigate candidate DNA methylation alterations that can distinguish melanoma from naevus; to develop an efficient and convenient methylation-specific quantitative real-time polymerase chain reaction assay (MS-qPCR) for the diagnosis of melanoma; and to validate the diagnostic performance of the MS-qPCR.

METHODS

We collected 145 formalin-fixed paraffin embedded tissue (FFPE) samples of malignant melanoma, 143 FFPE samples of benign naevus, 31 plasma samples from patients with melanoma and 37 plasma samples from healthy control skin between March 2018 and July 2024. The FFPE samples were divided into a discovery set, a training set and a validation set. PRAME, CLDN11 and SHOX2 promoter methylation levels were detected in the discovery set by pyrosequencing, to identify melanoma-specific methylation markers. Using these genes, we developed an efficient and convenient MS-qPCR diagnostic model and validated its diagnostic performance in the training set, validation set and plasma samples.

RESULTS

Pyrosequencing in the discovery set showed that PRAME and CLDN11 promoter methylation levels were significant diagnostic biomarkers of melanoma; no significant differences in SHOX2 promoter methylation were found between melanoma and naevi. MS-qPCR for the detection of PRAME and CLDN11 methylation levels was established. A diagnostic algorithm based on cycle threshold values was constructed and achieved high accuracy in the training set (sensitivity 94.3%, specificity 85.6%), validation set (sensitivity 84.5%, specificity 88.7%) and plasma samples (sensitivity 51.6%, specificity 83.8%). In terms of melanoma subtypes, the diagnostic algorithm enabled a high degree of discrimination between acral (sensitivity 89.9%, specificity 86.4%) and mucosal melanoma (sensitivity 100%, specificity 83.3%). More importantly, the diagnostic algorithm was able to distinguish early-stage melanoma from normal naevus, with an area under the curve of 0.879 and sensitivity of 77.3%.

CONCLUSIONS

The approach to detecting PRAME and CLDN11 methylation levels using MS-qPCR has high sensitivity and specificity in the differential diagnosis between benign and malignant melanocytic tumours. Using this approach in plasma is a promising and easily implementable strategy for early melanoma screening.