用于构建 microRNA 靶标调控网络的套索回归模型。

A Lasso regression model for the construction of microRNA-target regulatory networks.

机构信息

Beijing Institute of Radiation Medicine, State Key Laboratory of Proteomics, Beijing 100850, PR China.

出版信息

Bioinformatics. 2011 Sep 1;27(17):2406-13. doi: 10.1093/bioinformatics/btr410. Epub 2011 Jul 8.

DOI:10.1093/bioinformatics/btr410

PMID:21743061

Abstract

MOTIVATION

MicroRNAs have recently emerged as a major class of regulatory molecules involved in a broad range of biological processes and complex diseases. Construction of miRNA-target regulatory networks can provide useful information for the study and diagnosis of complex diseases. Many sequence-based and evolutionary information-based methods have been developed to identify miRNA-mRNA targeting relationships. However, as the amount of available miRNA and gene expression data grows, a more statistical and systematic method combining sequence-based binding predictions and expression-based correlation data becomes necessary for the accurate identification of miRNA-mRNA pairs.

RESULTS

We propose a Lasso regression model for the identification of miRNA-mRNA targeting relationships that combines sequence-based prediction information, miRNA co-regulation, RISC availability and miRNA/mRNA abundance data. By comparing this modelling approach with two other known methods applied to three different datasets, we found that the Lasso regression model has considerable advantages in both sensitivity and specificity. The regression coefficients in the model can be used to determine the true regulatory efficacies in tissues and was demonstrated using the miRNA target site type data. Finally, by constructing the miRNA regulatory networks in two stages of prostate cancer (PCa), we found the several significant miRNA-hubbed network modules associated with PCa metastasis. In conclusion, the Lasso regression model is a robust and informative tool for constructing the miRNA regulatory networks for diagnosis and treatment of complex diseases.

AVAILABILITY

The R program for predicting miRNA-mRNA targeting relationships using the Lasso regression model is freely available, along with the described datasets and resulting regulatory network, at http://biocompute.bmi.ac.cn/CZlab/alarmnet/. The source code is open for modification and application to other miRNA/mRNA expression datasets.

CONTACT

zhangcg@bmi.ac.cn

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

动机

microRNAs 最近成为涉及广泛生物过程和复杂疾病的主要调控分子类。构建 miRNA-靶标调控网络可为复杂疾病的研究和诊断提供有用信息。已经开发了许多基于序列和基于进化信息的方法来识别 miRNA-mRNA 靶向关系。然而，随着可用 miRNA 和基因表达数据量的增加，需要一种更具统计性和系统性的方法，将基于序列的结合预测和基于表达的相关数据结合起来，以准确识别 miRNA-mRNA 对。

结果

我们提出了一种用于识别 miRNA-mRNA 靶向关系的 Lasso 回归模型，该模型结合了基于序列的预测信息、miRNA 共调控、RISC 可用性和 miRNA/mRNA 丰度数据。通过将这种建模方法与应用于三个不同数据集的两种其他已知方法进行比较，我们发现 Lasso 回归模型在灵敏度和特异性方面都有很大的优势。模型中的回归系数可用于确定组织中的真实调控效率，并使用 miRNA 靶位点类型数据进行了验证。最后，通过构建前列腺癌（PCa）两个阶段的 miRNA 调控网络，我们发现了几个与 PCa 转移相关的显著 miRNA 中心网络模块。总之，Lasso 回归模型是构建用于复杂疾病诊断和治疗的 miRNA 调控网络的一种稳健且信息丰富的工具。