Zhang Wen-Wen, Ke Long-Feng, Chen Yu, Wu Chen-Yu, Lu Shu-Yi, Xie Yun-Li, Zhu Huan-Huan, Chen Hao, Chen Gang, Chen Yan-Ping
Department of Pathology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China.
Department of Molecular Pathology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, China.
Br J Dermatol. 2025 Aug 18;193(3):480-489. doi: 10.1093/bjd/ljaf169.
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.
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.
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.
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%.
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.
黑色素瘤的准确诊断可显著提高患者生存率。当肿瘤细胞局限于表皮或缺乏明显的核多形性时，单纯通过形态学将黑色素瘤与痣区分开来可能具有挑战性。
研究可区分黑色素瘤与痣的候选DNA甲基化改变；开发一种高效便捷的甲基化特异性定量实时聚合酶链反应检测法（MS-qPCR）用于黑色素瘤的诊断；并验证MS-qPCR的诊断性能。
我们收集了2018年3月至2024年7月期间的145份恶性黑色素瘤福尔马林固定石蜡包埋组织（FFPE）样本、143份良性痣FFPE样本、31份黑色素瘤患者的血浆样本和37份健康对照皮肤的血浆样本。FFPE样本被分为发现集、训练集和验证集。通过焦磷酸测序在发现集中检测PRAME、CLDN11和SHOX2启动子甲基化水平，以鉴定黑色素瘤特异性甲基化标志物。利用这些基因，我们开发了一种高效便捷的MS-qPCR诊断模型，并在训练集、验证集和血浆样本中验证其诊断性能。
发现集中的焦磷酸测序显示，PRAME和CLDN11启动子甲基化水平是黑色素瘤的重要诊断生物标志物；黑色素瘤与痣之间未发现SHOX2启动子甲基化有显著差异。建立了用于检测PRAME和CLDN11甲基化水平的MS-qPCR。构建了基于循环阈值的诊断算法，在训练集（敏感性94.3%，特异性85.6%）、验证集（敏感性84.5%，特异性88.7%）和血浆样本（敏感性51.6%，特异性83.8%）中均达到了较高的准确性。在黑色素瘤亚型方面，该诊断算法能够高度区分肢端黑色素瘤（敏感性89.9%，特异性86.4%）和黏膜黑色素瘤（敏感性100%，特异性83.3%）。更重要的是，该诊断算法能够区分早期黑色素瘤与正常痣，曲线下面积为0.879，敏感性为77.3%。
使用MS-qPCR检测PRAME和CLDN11甲基化水平的方法在良性和恶性黑素细胞肿瘤的鉴别诊断中具有高敏感性和特异性。在血浆中使用这种方法是早期黑色素瘤筛查的一种有前景且易于实施的策略。
