The transcriptome of a tumor contains detailed information about the disease. Although advances in sequencing technologies have generated larger data sets, there are still many questions about exactly how the transcriptome is regulated. One class of regulatory elements consists of microRNAs (or miRs), many of which are known to be associated with cancer. To better understand the relationships between miRs and cancers, we analyzed ∼9000 samples from 32 cancer types studied in The Cancer Genome Atlas. Our feature reduction algorithm found evidence for 21 biologically interpretable clusters of miRs, many of which were statistically associated with a specific type of cancer. Moreover, the clusters contain sufficient information to distinguish between most types of cancer. We then used linear models to measure, genome-wide, how much variation in gene expression could be explained by the 21 average expression values (“scores”) of the clusters. Based on the ∼20,000 per-gene R² values, we found that (1) mean differences between tissues of origin explain about 36% of variation; (2) the 21 miR cluster scores explain about 30% of the variation; and (3) combining tissue type with the miR scores explained about 56% of the total genome-wide variation in gene expression. Our analysis of poorly explained genes shows that they are enriched for olfactory receptor processes, sensory perception, and nervous system processing, which are necessary to receive and interpret signals from outside the organism. Therefore, it is reasonable for those genes to be always active and not get downregulated by miRs. In contrast, highly explained genes are characterized by genes translating to proteins necessary for transport, plasma membrane, or metabolic processes that are heavily regulated processes inside the cell. Other genetic regulatory elements such as transcription factors and methylation might help explain some of the remaining variation in gene expression.

中文翻译：

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使用 21 个 MicroRNA 解释基因表达。

肿瘤的转录组包含有关该疾病的详细信息。尽管测序技术的进步产生了更大的数据集，但关于转录组究竟是如何被调控的，仍然存在许多问题。一类调控元件由 microRNA（或 miR）组成，其中许多已知与癌症有关。为了更好地了解 miR 与癌症之间的关系，我们分析了来自癌症基因组图谱中研究的 32 种癌症类型的约 9000 个样本。我们的特征减少算法发现了 21 个生物学上可解释的 miR 簇的证据，其中许多在统计上与特定类型的癌症相关。此外，集群包含足够的信息来区分大多数类型的癌症。然后我们使用线性模型来测量，全基因组，聚类的 21 个平均表达值（“分数”）可以解释基因表达的多少变化。基于~20,000 每个基因R ²值，我们发现（1）起源组织之间的平均差异解释了大约 36% 的变异；(2) 21 个 miR 聚类分数解释了大约 30% 的变异；(3) 将组织类型与 miR 评分相结合，解释了基因表达中大约 56% 的全基因组变异。我们对解释不清的基因的分析表明，它们富含嗅觉受体过程、感觉知觉和神经系统处理，这些是接收和解释来自外部的信号所必需的生物体。因此，这些基因始终处于活跃状态并且不会被 miR 下调是合理的。相比之下，高度解释基因的特征是基因转化为运输、质膜或代谢过程所必需的蛋白质，这些过程是细胞内受到严格调控的过程。其他遗传调控元件如转录因子和甲基化可能有助于解释基因表达中的一些剩余变异。