Peng Chen, Wang Minghui, Shen Yi, Feng Huanqing, Li Ao
School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, China.
PLoS One. 2013 Oct 29;8(10):e78197. doi: 10.1371/journal.pone.0078197. eCollection 2013.
As one of the most common types of co-regulatory motifs, feed-forward loops (FFLs) control many cell functions and play an important role in human cancers. Therefore, it is crucial to reconstruct and analyze cancer-related FFLs that are controlled by transcription factor (TF) and microRNA (miRNA) simultaneously, in order to find out how miRNAs and TFs cooperate with each other in cancer cells and how they contribute to carcinogenesis. Current FFL studies rely on predicted regulation information and therefore suffer the false positive issue in prediction results. More critically, FFLs generated by existing approaches cannot represent the dynamic and conditional regulation relationship under different experimental conditions.
METHODOLOGY/PRINCIPAL FINDINGS: In this study, we proposed a novel filter-wrapper feature selection method to accurately identify co-regulatory mechanism by incorporating prior information from predicted regulatory interactions with parallel miRNA/mRNA expression datasets. By applying this method, we reconstructed 208 and 110 TF-miRNA co-regulatory FFLs from human pan-cancer and prostate datasets, respectively. Further analysis of these cancer-related FFLs showed that the top-ranking TF STAT3 and miRNA hsa-let-7e are key regulators implicated in human cancers, which have regulated targets significantly enriched in cellular process regulations and signaling pathways that are involved in carcinogenesis.
CONCLUSIONS/SIGNIFICANCE: In this study, we introduced an efficient computational approach to reconstruct co-regulatory FFLs by accurately identifying gene co-regulatory interactions. The strength of the proposed feature selection method lies in the fact it can precisely filter out false positives in predicted regulatory interactions by quantitatively modeling the complex co-regulation of target genes mediated by TFs and miRNAs simultaneously. Moreover, the proposed feature selection method can be generally applied to other gene regulation studies using parallel expression data with respect to different biological contexts.
作为最常见的共调控基序类型之一，前馈环（FFL）控制着许多细胞功能，并在人类癌症中发挥重要作用。因此，重建和分析由转录因子（TF）和微小RNA（miRNA）同时控制的癌症相关FFL至关重要，以便了解miRNA和TF在癌细胞中如何相互协作以及它们如何促进肿瘤发生。当前的FFL研究依赖于预测的调控信息，因此预测结果存在假阳性问题。更关键的是，现有方法生成的FFL无法代表不同实验条件下的动态和条件调控关系。
方法/主要发现：在本研究中，我们提出了一种新颖的过滤-包装特征选择方法，通过将预测调控相互作用的先验信息与平行的miRNA/mRNA表达数据集相结合，准确识别共调控机制。通过应用该方法，我们分别从人类泛癌数据集和前列腺数据集中重建了208个和110个TF-miRNA共调控FFL。对这些癌症相关FFL的进一步分析表明，排名靠前的TF STAT3和miRNA hsa-let-7e是人类癌症中的关键调节因子，它们调控的靶标在细胞过程调控和参与肿瘤发生的信号通路中显著富集。
结论/意义：在本研究中，我们引入了一种有效的计算方法，通过准确识别基因共调控相互作用来重建共调控FFL。所提出的特征选择方法的优势在于，它可以通过同时定量建模TF和miRNA介导的靶基因复杂共调控，精确滤除预测调控相互作用中的假阳性。此外，所提出的特征选择方法通常可应用于其他使用不同生物学背景下平行表达数据的基因调控研究。
