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CLIN CANCER RES：阻止特异性DNA甲基化特征用于诊断甲状腺结节

2019-01-27 MedSci MedSci原创

甲状腺结节DNA甲基化组织特异性
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由于恶性结节和良性结节之间细胞学特征的重叠，甲状腺癌常常难以诊断。从而导致许多没有癌症的患者不必要地切除了甲状腺。分子学方法虽然可以在细胞病理学诊断基础上得到一些改进，常常无法为甲状腺结节提供正确的诊断。这些方法的基础是癌症和周围正常甲状腺组织之间存在差异，并假设邻近组织与良性结节相同。然而，与周围正常组织相比，良性甲状腺结节可能包含可在癌症中发现的遗传改变。CLIN CANCER RES近期发表

由于恶性结节和良性结节之间细胞学特征的重叠，甲状腺癌常常难以诊断。从而导致许多没有癌症的患者不必要地切除了甲状腺。分子学方法虽然可以在细胞病理学诊断基础上得到一些改进，常常无法为甲状腺结节提供正确的诊断。这些方法的基础是癌症和周围正常甲状腺组织之间存在差异，并假设邻近组织与良性结节相同。然而，与周围正常组织相比，良性甲状腺结节可能包含可在癌症中发现的遗传改变。CLIN CANCER RES近期发表了一篇文章，研究新的甲状腺结节诊断方法。

作者使用全基因组单碱基DNA甲基化分析评估了109个甲状腺组织中的DNA甲基化情况。并在含有65个甲状腺结节的回顾性队列中进行验证。研究结果表明，周围正常组织，良性结节和癌症之间测序结果存在显着差异。这些组织特异性特征与癌症相关基因密切相关。基于这些特征，作者开发了一种新的表观遗传学方法用于甲状腺诊断。验证队列结果表明，该方法的预估特异性为97％，灵敏度为100％，阳性预测值为97％，阴性预测值为100％。

文章最后认为，表观遗传学检测诊断甲状腺结节的诊断准确性非常出色。

原始出处：

John H. Yim, Audrey H. Choi, et al. Identification of Tissue-Specific DNA Methylation Signatures for Thyroid Nodule Diagnostics. CLIN CANCER RES. January 2019 doi: 10.1158/1078-0432.CCR-18-0841

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    2019-07-12 jinweidong

    #特异性#

    0

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    2019-01-28 Y—xianghai

    学习了长知识

    0

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    2019-01-28 Y—xianghai

    学习了新知识

    0

  4. 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topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20201006/4db877c2887649beb09cb5effd0b38c7/916f66edd48a4e77b20ebc3e14b6e45c.jpg, createdBy=141e1980133, createdName=Y—xianghai, createdTime=Mon Jan 28 13:14:40 CST 2019, time=2019-01-28, status=1, ipAttribution=)]
    2019-01-28 Y—xianghai

    学习了长知识

    0

  5. 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topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20201006/4db877c2887649beb09cb5effd0b38c7/916f66edd48a4e77b20ebc3e14b6e45c.jpg, createdBy=141e1980133, createdName=Y—xianghai, createdTime=Mon Jan 28 13:14:40 CST 2019, time=2019-01-28, status=1, ipAttribution=)]
    2019-01-28 Y—xianghai

    学习了新知识

    0

  6. 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topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20201006/4db877c2887649beb09cb5effd0b38c7/916f66edd48a4e77b20ebc3e14b6e45c.jpg, createdBy=141e1980133, createdName=Y—xianghai, createdTime=Mon Jan 28 13:14:40 CST 2019, time=2019-01-28, status=1, ipAttribution=)]
    2019-01-28 Y—xianghai

    学习了长知识

    0

  7. 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    2019-01-28 Y—xianghai

    学习了新知识

    0

  8. 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topicName=null, topicId=null, topicList=[], attachment=null, authenticateStatus=null, createdAvatar=https://img.medsci.cn/20201006/4db877c2887649beb09cb5effd0b38c7/916f66edd48a4e77b20ebc3e14b6e45c.jpg, createdBy=141e1980133, createdName=Y—xianghai, createdTime=Mon Jan 28 13:14:40 CST 2019, time=2019-01-28, status=1, ipAttribution=)]
    2019-01-28 Y—xianghai

    学习了长知识

    0

  9. 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    2019-01-28 Y—xianghai

    学习了新知识

    0

  10. 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    2019-01-28 Y—xianghai

    学习了长知识

    0

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