整合分析揭示了癌症中由microRNAs调节的中断途径
Integrative analysis reveals disrupted pathways regulated by microRNAs in cancer
摘要：
微小RNA（microRNAs，miRNAs）是一种内源性小分子，在转录后调节基因表达。尽管miRNAs的差异表达与许多疾病（包括癌症）有关，但其潜在的作用机制仍不清楚。由于每个miRNA都可以靶向多个基因，因此miRNA可能对整个通路的整体行为具有潜在的功能意义。在这里，我们使用一种先验整合途径信息的新方法，通过对miRNA和mRNA表达数据的综合分析来研究miRNA失调的功能后果。通过寻找健康组织和肿瘤组织之间不同的miRNA通路相关性，我们确定了在癌症中被破坏的系统水平上的特定关系。我们的方法的动机是假设，如果一个miRNA和通路相关，那么miRNA的表达和通路中基因的集体行为将相关。因此，我们首先使用Isomap获得基于表达的途径活性摘要，Isomap是一种降维方法，可以在高维数据中表达非线性结构。然后，我们寻找与表型之间的通路汇总表现出差异相关性的miRNA，作为发现肿瘤中异常miRNA通路共调节的手段。我们将我们的方法应用于癌症数据，使用来自癌症基因组图谱（TCGA）的基因和miRNA表达数据集，并比较来自四种组织（乳腺、前列腺、肺和肝）的健康和肿瘤样本之间的${\sim}10^5$ miRNA通路关系。我们发现的许多标记对都有破坏癌症的生物学基础。
英文摘要：
MicroRNAs (miRNAs) are small endogenous regulatory molecules that modulate gene expression post-transcriptionally. Although differential expression of miRNAs have been implicated in many diseases (including cancers), the underlying mechanisms of action remain unclear. Because each miRNA can target multiple genes, miRNAs may potentially have functional implications for the overall behavior of entire pathways. Here we investigate the functional consequences of miRNA dysregulation through an integrative analysis of miRNA and mRNA expression data using a novel approach that incorporates pathway information a priori. By searching for miRNA-pathway associations that differ between healthy and tumor tissue, we identify specific relationships at the systems-level which are disrupted in cancer. Our approach is motivated by the hypothesis that if a miRNA and pathway are associated, then the expression of the miRNA and the collective behavior of the genes in a pathway will be correlated. As such, we first obtain an expression-based summary of pathway activity using Isomap, a dimension reduction method which can articulate nonlinear structure in high-dimensional data. We then search for miRNAs that exhibit differential correlations with the pathway summary between phenotypes as a means of finding aberrant miRNA-pathway coregulation in tumors. We apply our method to cancer data using gene and miRNA expression datasets from The Cancer Genome Atlas (TCGA) and compare ${\sim}10^5$ miRNA-pathway relationships between healthy and tumor samples from four tissues (breast, prostate, lung, and liver). Many of the flagged pairs we identify have a biological basis for disruption in cancer.
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