辨識競爭性內源核糖核酸與微型核糖核酸三元體之新穎 演算法開發

Abstract

在生物學中，了解身體功能在分子間的交互作用是極其重要的。近年來的研究顯示微型核糖核酸和目標信使核糖核酸之間的交互作用並非單方向及單調的上升或下降，而且這種調節機制存在於許多疾病。在微型核糖核酸的目標基因當中，一個微型核糖核酸可以調控多個靶基因並且相同靶基因可以被不同微型微型核酸調控，其中有些基因被命名為競爭性內源核糖核酸，在能被同一微型核糖核酸調控的情況下，這些基因彼此便構成了一種競爭關係，由於至今已被證實為競爭性內源核糖核酸的基因數量並不多，因此，探索這種複雜的調節機制成為近年來一大挑戰。至今已發展出許多辨識競爭性內源核糖核酸及其動態調節系統的演算法，絕大部份的演算法依據微型核糖核酸的表現量將其分組後進行後續分析，然而，微型核糖核酸的表現量為一連續變數而非離散，若視為離散變數進行分組可能將其調節作用阻斷於分析時被忽略。對此，我們基於隨機漫步的概念以及環狀二元分段法發展一個新的演算法。在隨機漫步的方法中，分數會隨著下一步移動而上下波動，最終的統計量為距離0最遠的分數接著做排列檢定(permutation test)，接著用環狀二元分段法得到兩基因發生最高度相關的微型核糖核酸表現量。在模擬研究中顯示我們所提出的演算法能正確的辨識競爭性內源核糖核酸與微型核糖核酸間的交互作用，此外，我們將其應用於TCGA的兩種癌症資料，發現有些共同的微型核糖核酸與競爭性內源核糖核酸於兩筆資料中被找到，且由文獻證實與非小細胞肺癌(non-small cell lung cancer)有關。基於模擬研究與實際資料應用的結果，我們的方法能有效辨識這種特殊的調節機制，特別的是，我們的方法能取得微型核糖核酸在特定區域的表現量值下才會發生此種交互作用。我們相信此演算法能提供競爭性內源與微型核糖核酸調節機制更進一步的了解。

Understanding physical and functional interactions between molecules in living systems is of vital importance in biology. Recent studies have shown that the interaction of microRNA (miRNA) and mRNA is not unidirectional and monotonic, which has been suggested as an important regulating mechanism in many diseases. Among the target genes of miRNAs, some of them are named as competing endogenous RNAs (ceRNAs), and their expression levels affected by the expression level of miRNAs can be regulated through competing for a pool of common binding miRNAs. Therefore, challenge arises when trying to systematically explore the association of miRNA and its target genes. Several algorithms have been developed to identify ceRNAs and their dynamic regulating systems. Most of the algorithms divide a miRNA into different groups based on its expression level and then perform the analysis accordingly. However, the expression level of a miRNA is actually a continuous variable instead of a discrete variable. To address this issue, we developed a new algorithm based on the random walk concept and circular binary algorithm. The score obtained from the random walk method was the maximum deviation from zero weighted by the correlation within each window. We then applied the circular binary algorithm to get the peaks from the miRNA expression levels across samples. Simulation studies demonstrate our proposed algorithm can accurately identify a ceRNA-miRNA triplet with high correlation. Also, we applied the algorithm to two TCGA cancers. Some common bridging miRNA and ceRNAs were found in two cancers and were verified by previous studies. Based on the results of simulation and application, our methods are effective and feasible to identify ceRNA-miRNA triplet. In particular, we can also capture the multiple peaks of correlation at specific miRNA expression levels. We believed that our algorithm is able to provide an insight in miRNA-modulated ceRNA regulatory mechanisms.