Glostrup Research Institute, Glostrup University Hospital, DK-2600 Glostrup, Denmark.
BMC Genomics. 2011 Feb 4;12:97. doi: 10.1186/1471-2164-12-97.
Several approaches have been developed for miRNA target prediction, including methods that incorporate expression profiling. However the methods are still in need of improvements due to a high false discovery rate. So far, none of the methods have used independent component analysis (ICA). Here, we developed a novel target prediction method based on ICA that incorporates both seed matching and expression profiling of miRNA and mRNA expressions. The method was applied on a cellular model of type 1 diabetes.
Microarray profiling identified eight miRNAs (miR-124/128/192/194/204/375/672/708) with differential expression. Applying ICA on the mRNA profiling data revealed five significant independent components (ICs) correlating to the experimental conditions. The five ICs also captured the miRNA expressions by explaining > 97% of their variance. By using ICA, seven of the eight miRNAs showed significant enrichment of sequence predicted targets, compared to only four miRNAs when using simple negative correlation. The ICs were enriched for miRNA targets that function in diabetes-relevant pathways e.g. type 1 and type 2 diabetes and maturity onset diabetes of the young (MODY).
In this study, ICA was applied as an attempt to separate the various factors that influence the mRNA expression in order to identify miRNA targets. The results suggest that ICA is better at identifying miRNA targets than negative correlation. Additionally, combining ICA and pathway analysis constitutes a means for prioritizing between the predicted miRNA targets. Applying the method on a model of type 1 diabetes resulted in identification of eight miRNAs that appear to affect pathways of relevance to disease mechanisms in diabetes.
已经开发出几种 miRNA 靶标预测方法，包括整合表达谱的方法。但是，由于假阳性率高，这些方法仍需要改进。到目前为止，还没有一种方法使用独立成分分析（ICA）。在这里，我们开发了一种新的基于 ICA 的靶标预测方法，该方法结合了 miRNA 和 mRNA 表达的种子匹配和表达谱。该方法应用于 1 型糖尿病的细胞模型。
微阵列分析确定了 8 个具有差异表达的 miRNA（miR-124/128/192/194/204/375/672/708）。在 mRNA 分析数据上应用 ICA 揭示了与实验条件相关的五个显著独立成分（IC）。这五个 IC 还通过解释 >97%的方差来捕获 miRNA 的表达。通过使用 ICA，与仅使用简单负相关时相比，这 8 个 miRNA 中有 7 个显示出序列预测靶标显著富集。IC 富集了与糖尿病相关途径（例如 1 型和 2 型糖尿病以及青少年发病型糖尿病（MODY））中功能相关的 miRNA 靶标。
在这项研究中，ICA 被应用于尝试分离影响 mRNA 表达的各种因素，以识别 miRNA 靶标。结果表明，ICA 比负相关更能识别 miRNA 靶标。此外，将 ICA 和途径分析相结合构成了一种在预测的 miRNA 靶标之间进行优先级排序的方法。将该方法应用于 1 型糖尿病模型，鉴定了 8 个似乎影响糖尿病发病机制相关途径的 miRNA。
