Unraveling the hidden heterogeneities of breast cancer based on functional miRNA cluster.
Unraveling the hidden heterogeneities of breast cancer based on functional miRNA cluster.
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基于功能性 miRNA 簇揭示乳腺癌隐藏的异质性
DOI：
10.1371/journal.pone.0087601
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Peng L
中科院分区：
文献类型：
--
作者：
Li L;Liu C;Wang F;Miao W;Zhang J;Kang Z;Chen Y;Peng L
越来越清楚的是，目前临床表型的分类与分子异质性混合在一起，这可能会影响患者的治疗效果。因此，使用现代大规模基因组方法定义隐藏的分子独特疾病对于改进临床实践和改进干预策略是有用的。鉴于microRNA表达谱提供了一种强大的方法来剖析复杂疾病的隐藏遗传异质性，本研究的目的是开发一种生物信息学方法来识别导致复杂临床表型隐藏亚型的microRNA特征。该方法的基本策略是利用双向超顺磁聚类技术对样本和特征空间进行迭代划分，从而识别出最优的miRNA聚类。我们通过确定簇内microrna的共表达一致性和染色体位置来评估获得的最佳miRNA簇，并得出结论，最佳miRNA簇可以导致疾病样本的自然分割。我们将提出的方法应用于一个公开可用的乳腺癌患者微阵列数据集，这些数据集具有众所周知的异质性表型。我们获得了13个microrna的特征子集，可将71例乳腺癌患者分为5个亚型，5年总生存率差异显著（分别为45%、82.4%、70.6%、100%和60%，p = 0.008）。通过建立特征子集的多变量Cox比例风险预测模型，我们确定has-miR-146b是最显著的预测因子之一（p = 0.045；风险比= 0.39）。该算法是一种很有前途的计算策略，用于解剖复杂疾病的隐藏遗传异质性，并将对改善癌症的诊断和治疗有价值。
It has become increasingly clear that the current taxonomy of clinical phenotypes is mixed with molecular heterogeneity, which potentially affects the treatment effect for involved patients. Defining the hidden molecular-distinct diseases using modern large-scale genomic approaches is therefore useful for refining clinical practice and improving intervention strategies. Given that microRNA expression profiling has provided a powerful way to dissect hidden genetic heterogeneity for complex diseases, the aim of the study was to develop a bioinformatics approach that identifies microRNA features leading to the hidden subtyping of complex clinical phenotypes. The basic strategy of the proposed method was to identify optimal miRNA clusters by iteratively partitioning the sample and feature space using the two-ways super-paramagnetic clustering technique. We evaluated the obtained optimal miRNA cluster by determining the consistency of co-expression and the chromosome location among the within-cluster microRNAs, and concluded that the optimal miRNA cluster could lead to a natural partition of disease samples. We applied the proposed method to a publicly available microarray dataset of breast cancer patients that have notoriously heterogeneous phenotypes. We obtained a feature subset of 13 microRNAs that could classify the 71 breast cancer patients into five subtypes with significantly different five-year overall survival rates (45%, 82.4%, 70.6%, 100% and 60% respectively; p = 0.008). By building a multivariate Cox proportional-hazards prediction model for the feature subset, we identified has-miR-146b as one of the most significant predictor (p = 0.045; hazard ratios = 0.39). The proposed algorithm is a promising computational strategy for dissecting hidden genetic heterogeneity for complex diseases, and will be of value for improving cancer diagnosis and treatment.
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影响因子：
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作者：
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