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首页> 外文期刊>Journal of Hematology and Oncology >An imprinted non-coding genomic cluster at 14q32 defines clinically relevant molecular subtypes in osteosarcoma across multiple independent datasets
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An imprinted non-coding genomic cluster at 14q32 defines clinically relevant molecular subtypes in osteosarcoma across multiple independent datasets

机译:14q32的印迹非编码基因组簇跨多个独立数据集定义了骨肉瘤中临床相关的分子亚型

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BackgroundA microRNA (miRNA) collection on the imprinted 14q32 MEG3 region has been associated with outcome in osteosarcoma. We assessed the clinical utility of this miRNA set and their association with methylation status. MethodsWe integrated coding and non-coding RNA data from three independent annotated clinical osteosarcoma cohorts ( n =?65, n =?27, and n =?25) and miRNA and methylation data from one in vitro (19 cell lines) and one clinical (NCI Therapeutically Applicable Research to Generate Effective Treatments (TARGET) osteosarcoma dataset, n =?80) dataset. We used time-dependent receiver operating characteristic (tdROC) analysis to evaluate the clinical value of candidate miRNA profiles and machine learning approaches to compare the coding and non-coding transcriptional programs of high- and low-risk osteosarcoma tumors and high- versus low-aggressiveness cell lines. In the cell line and TARGET datasets, we also studied the methylation patterns of the MEG3 imprinting control region on 14q32 and their association with miRNA expression and tumor aggressiveness. ResultsIn the tdROC analysis, miRNA sets on 14q32 showed strong discriminatory power for recurrence and survival in the three clinical datasets. High- or low-risk tumor classification was robust to using different microRNA sets or classification methods. Machine learning approaches showed that genome-wide miRNA profiles and miRNA regulatory networks were quite different between the two outcome groups and mRNA profiles categorized the samples in a manner concordant with the miRNAs, suggesting potential molecular subtypes. Further, miRNA expression patterns were reproducible in comparing high-aggressiveness versus low-aggressiveness cell lines. Methylation patterns in the MEG3 differentially methylated region (DMR) also distinguished high-aggressiveness from low-aggressiveness cell lines and were associated with expression of several 14q32 miRNAs in both the cell lines and the large TARGET clinical dataset. Within the limits of available CpG array coverage, we observed a potential methylation-sensitive regulation of the non-coding RNA cluster by CTCF, a known enhancer-blocking factor. ConclusionsLoss of imprinting/methylation changes in the 14q32 non-coding region defines reproducible previously unrecognized osteosarcoma subtypes with distinct transcriptional programs and biologic and clinical behavior. Future studies will define the precise relationship between 14q32 imprinting, non-coding RNA expression, genomic enhancer binding, and tumor aggressiveness, with possible therapeutic implications for both early- and advanced-stage patients.
机译:背景印迹14q32 MEG3区域的microRNA(miRNA)收集与骨肉瘤的预后相关。我们评估了该miRNA集的临床效用及其与甲基化状态的关联。方法我们整合了来自三个独立的带注释的临床骨肉瘤队列(n =?65,n =?27和n =?25)的编码和非编码RNA数据以及来自一个体外(19个细胞系)和一个临床的miRNA和甲基化数据(NCI产生有效治疗的治疗性应用研究(TARGET)骨肉瘤数据集,n =?80)。我们使用时间依赖性接收器操作特征(tdROC)分析来评估候选miRNA谱的临床价值,并使用机器学习方法比较高危和低危骨肉瘤肿瘤以及高危和低危骨肉瘤的编码和非编码转录程序侵略性细胞系。在细胞系和TARGET数据集中,我们还研究了14q32上MEG3印迹控制区的甲基化模式及其与miRNA表达和肿瘤侵袭性的关系。结果在tdROC分析中,在三个临床数据集中14q32上的miRNA集显示出强大的区分力,可用于复发和生存。高风险或低风险的肿瘤分类对于使用不同的microRNA集或分类方法是可靠的。机器学习方法表明,两个结果组之间的全基因组miRNA谱和miRNA调节网络有很大不同,并且mRNA谱以与miRNA一致的方式对样品进行了分类,表明了潜在的分子亚型。此外,在比较高攻击性和低攻击性细胞系时,miRNA表达模式是可重现的。 MEG3差异甲基化区域(DMR)中的甲基化模式也将高攻击性与低攻击性细胞系区分开,并且与细胞系和大型TARGET临床数据集中的几个14q32 miRNA的表达相关。在可用的CpG阵列覆盖范围内,我们观察到了CTCF(一种已知的增强子阻断因子)对非编码RNA簇的潜在甲基化敏感调控。结论14q32非编码区的印记/甲基化变化丢失定义了可重现的先前无法识别的骨肉瘤亚型,具有不同的转录程序以及生物学和临床行为。未来的研究将确定14q32印迹,非编码RNA表达,基因组增强子结合和肿瘤侵袭性之间的确切关系,对早期和晚期患者均可能具有治疗意义。

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