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Specific breast cancer prognosis‐subtype distinctions based on DNA methylation patterns

机译:基于DNA甲基化模式的特定乳腺癌预后亚型差异

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Tumour heterogeneity is an obstacle to effective breast cancer diagnosis and therapy. DNA methylation is an important regulator of gene expression, thus characterizing tumour heterogeneity by epigenetic features can be clinically informative. In this study, we explored specific prognosis‐subtypes based on DNA methylation status using 669 breast cancers from the TCGA database. Nine subgroups were distinguished by consensus clustering using 3869 CpGs that significantly influenced survival. The specific DNA methylation patterns were reflected by different races, ages, tumour stages, receptor status, histological types, metastasis status and prognosis. Compared with the PAM50 subtypes, which use gene expression clustering, DNA methylation subtypes were more elaborate and classified the Basal‐like subtype into two different prognosis‐subgroups. Additionally, 1252 CpGs (corresponding to 888 genes) were identified as specific hyper/hypomethylation sites for each specific subgroup. Finally, a prognosis model based on Bayesian network classification was constructed and used to classify the test set into DNA methylation subgroups, which corresponded to the classification results of the train set. These specific classifications by DNA methylation can explain the heterogeneity of previous molecular subgroups in breast cancer and will help in the development of personalized treatments for the new specific subtypes.
机译:肿瘤异质性是有效乳腺癌诊断和治疗的障碍。 DNA甲基化是基因表达的重要调节剂,因此通过表观遗传学特征表征肿瘤异质性可能具有临床意义。在这项研究中,我们使用TCGA数据库中的669种乳腺癌,根据DNA甲基化状态探索了特定的预后亚型。通过使用3869个CpG进行共识聚类,将9个亚组区分开来,这显着影响了生存率。不同种族,年龄,肿瘤分期,受体状态,组织学类型,转移状态和预后反映出特定的DNA甲基化模式。与使用基因表达聚类的PAM50亚型相比,DNA甲基化亚型更加精细,并将Basal-like亚型分为两个不同的预后亚组。另外,鉴定出1252个CpG(对应于888个基因)作为每个特定亚组的特定高/低甲基化位点。最后,建立了基于贝叶斯网络分类的预测模型,并将其用于将测试集分类为DNA甲基化亚组,这与训练集的分类结果相对应。通过DNA甲基化的这些特定分类可以解释乳腺癌中先前分子亚组的异质性,并将有助于开发针对新的特定亚型的个性化治疗方法。

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