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A multi-marker assay to distinguish malignant melanomas from benign nevi

机译:区分恶性黑色素瘤与良性痣的多标记检测

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摘要

The histopathological diagnosis of melanoma can be challenging. No currently used molecular markers accurately distinguish between nevus and melanoma. Recent transcriptome analyses have shown the differential expression of several genes in melanoma progression. Here, we describe a multi-marker diagnostic assay using 5 markers (ARPC2, FN1, RGS1, SPP1, and WNT2) overex-pressed in melanomas. Immunohistochemical marker expression was analyzed in 693 melanocytic neoplasms comprising a training set (tissue microarray of 534 melanomas and nevi), and 4 independent validation sets: tissue sections of melanoma arising in a nevus; dysplastic nevi; Spitz nevi; and misdiagnosed melanocytic neoplasms. Both intensity and pattern of expression were scored for each marker. Based on the differential expression of these 5 markers between nevi and melanomas in the training set, a diagnostic algorithm was obtained. Using this algorithm, the lesions in the validation sets were diagnosed as nevus or melanoma, and the results were compared with the known histological diagnoses. Both the intensity and pattern of expression of each marker were significantly different in melanomas compared to nevi. The diagnostic algorithm exploiting these differences achieved a specificity of 95% and a sensitivity of 91% in the training set. In the validation sets, the multi-marker assay correctly diagnosed a high percentage of melanomas arising in a nevus, Spitz nevi, dysplastic nevi, and misdiagnosed lesions. The multi-marker assay described here can aid in the diagnosis of melanoma.
机译:黑色素瘤的组织病理学诊断可能具有挑战性。当前没有使用的分子标记物能准确地区分痣和黑色素瘤。最近的转录组分析显示了黑色素瘤进展中几个基因的差异表达。在这里,我们描述了使用在黑素瘤中过表达的5个标记(ARPC2,FN1,RGS1,SPP1和WNT2)进行的多标记诊断测定。在包括训练组(534个黑色素瘤和痣的组织微阵列)和4个独立的验证组的693个黑色素细胞瘤中分析了免疫组织化学标记物的表达。增生痣Spitz nevi;和误诊的黑素瘤。对每种标记物都对表达强度和表达模式进行了评分。根据训练集中的痣和黑色素瘤之间这5个标记的差异表达,获得了诊断算法。使用该算法,将验证集中的病变诊断为痣或黑色素瘤,并将结果与​​已知的组织学诊断结果进行比较。与痣相比,黑色素瘤中每种标志物的表达强度和表达方式均显着不同。利用这些差异的诊断算法在训练集中实现了95%的特异性和91%的敏感性。在验证集中,多标记测定法正确诊断了由痣,斯皮茨痣,增生性痣和误诊病变引起的黑色素瘤的高百分比。此处描述的多标记分析可帮助诊断黑色素瘤。

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  • 作者单位

    Auerback Melanoma Research Laboratory, Comprehensive Cancer Center and Department of Dermatology, Comprehensive Cancer Center University of California, San Francisco, CA 94115;

    Auerback Melanoma Research Laboratory, Comprehensive Cancer Center and Department of Dermatology, Comprehensive Cancer Center University of California, San Francisco, CA 94115;

    Auerback Melanoma Research Laboratory, Comprehensive Cancer Center and Department of Dermatology, Comprehensive Cancer Center University of California, San Francisco, CA 94115;

    Auerback Melanoma Research Laboratory, Comprehensive Cancer Center and Department of Dermatology, Comprehensive Cancer Center University of California, San Francisco, CA 94115;

    Departments of Urology, University of California, San Francisco, CA 94115;

    Departments of Surgery, University of California, San Francisco, CA 94115;

    Auerback Melanoma Research Laboratory, Biostatistics Core, Comprehensive Cancer Center, University of California, San Francisco, CA 94115;

    Departments of Urology, University of California, San Francisco, CA 94115;

    Auerback Melanoma Research Laboratory, Comprehensive Cancer Center and Department of Dermatology, Comprehensive Cancer Center University of California, San Francisco, CA 94115;

    Auerback Melanoma Research Laboratory, Comprehensive Cancer Center and Department of Dermatology, Comprehensive Cancer Center University of California, San Francisco, CA 94115;

  • 收录信息 美国《科学引文索引》(SCI);美国《生物学医学文摘》(MEDLINE);美国《化学文摘》(CA);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    biomarkers; diagnosis; microarray analysis;

    机译:生物标志物诊断;芯片分析;
  • 入库时间 2022-08-18 00:41:56

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