Methylation-Based Test for Diagnosis of Benign and Malignant Melanocytoma

Abstract Background Accurate diagnosis of melanomas significantly improves patient survival rates. Distinguishing melanomas from nevi purely by morphology by pathologists can be challenging when the neoplastic cells are confined to the epidermis or lack marked nuclear pleomorphism. Objectives To investigate candidate DNA methylation alterations that can distinguish melanoma from nevus, to develop an efficient and convenient methylation-specific quantitative real time PCR assay (MS-qPCR) for the diagnosis of melanoma and to validate its diagnostic performance. Methods We collected 145 formalin-fixed paraffin embedded tissue (FFPE) samples of malignant melanoma, 143 FFPE samples of benign nevus, 31 plasma samples of melanoma and 37 plasma samples of healthy controls between March 2018 and July 2024. The FFPE samples were divided into the discovery set, training set and validation set. The PRAME, CLDN11 andSHOX2 promoter methylation levels were detected in the discovery set by pyrosequencing to identify melanoma-specific methylation markers. Using statistically different 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 the PRAME and CLDN11 promoter methylation levels were significant diagnostic biomarkers of melanoma; no significant differences in SHOX2 promoter methylation were found between melanoma and nevi. MS-qPCR for the detection of PRAME andCLDN11 methylation levels was established, which allowed quantitative analysis of samples with as little as 1% diluted samples. A diagnostic algorithm based on CT values was constructed and achieved high accuracy in the training set (sensitivity=94.25%, specificity=85.56%), validation set (sensitivity=84.48%, specificity=88.68%) and plasma samples (sensitivity=51.61%, specificity=83.78%). In terms of different subtypes, the diagnostic algorithm enabled a high degree of discrimination for acral melanoma (sensitivity=89.90%, specificity=86.36%) and mucosal melanoma (sensitivity=100%, specificity=83.3%). More importantly, the diagnostic algorithm was able to distinguish early-stage melanoma from normal nevus, with an AUC of 0.879 and sensitivity of 77.27%. Conclusions The approach detecting PRAME and CLDN11 methylation levels using MS-qPCR has high sensitivity and specificity in the differential diagnosis between benign and malignant melanocytic tumors. Using this approach in plasma is a promising and easily implementable strategy for early screening of melanoma.