Human Genome Center, Institute of Medical Science, University of Tokyo, Minato-ku, Tokyo, Japan.
PLoS One. 2011;6(6):e20804. doi: 10.1371/journal.pone.0020804. Epub 2011 Jun 7.
Patient-specific analysis of molecular networks is a promising strategy for making individual risk predictions and treatment decisions in cancer therapy. Although systems biology allows the gene network of a cell to be reconstructed from clinical gene expression data, traditional methods, such as bayesian networks, only provide an averaged network for all samples. Therefore, these methods cannot reveal patient-specific differences in molecular networks during cancer progression. In this study, we developed a novel statistical method called NetworkProfiler, which infers patient-specific gene regulatory networks for a specific clinical characteristic, such as cancer progression, from gene expression data of cancer patients. We applied NetworkProfiler to microarray gene expression data from 762 cancer cell lines and extracted the system changes that were related to the epithelial-mesenchymal transition (EMT). Out of 1732 possible regulators of E-cadherin, a cell adhesion molecule that modulates the EMT, NetworkProfiler, identified 25 candidate regulators, of which about half have been experimentally verified in the literature. In addition, we used NetworkProfiler to predict EMT-dependent master regulators that enhanced cell adhesion, migration, invasion, and metastasis. In order to further evaluate the performance of NetworkProfiler, we selected Krueppel-like factor 5 (KLF5) from a list of the remaining candidate regulators of E-cadherin and conducted in vitro validation experiments. As a result, we found that knockdown of KLF5 by siRNA significantly decreased E-cadherin expression and induced morphological changes characteristic of EMT. In addition, in vitro experiments of a novel candidate EMT-related microRNA, miR-100, confirmed the involvement of miR-100 in several EMT-related aspects, which was consistent with the predictions obtained by NetworkProfiler.
患者特异性分子网络分析是一种很有前途的策略，可以对癌症治疗中的个体风险预测和治疗决策进行个体化。虽然系统生物学允许从临床基因表达数据中重建细胞的基因网络，但传统方法，如贝叶斯网络，仅为所有样本提供一个平均网络。因此，这些方法无法揭示癌症进展过程中分子网络的患者特异性差异。在这项研究中，我们开发了一种新的统计方法，称为 NetworkProfiler，它可以从癌症患者的基因表达数据中推断出特定临床特征（如癌症进展）的患者特异性基因调控网络。我们将 NetworkProfiler 应用于来自 762 个癌细胞系的微阵列基因表达数据，并提取了与上皮-间充质转化（EMT）相关的系统变化。在 1732 个可能调节 E-钙粘蛋白的调节剂中，E-钙粘蛋白是一种调节 EMT 的细胞黏附分子，NetworkProfiler 鉴定出 25 个候选调节剂，其中约一半在文献中得到了实验验证。此外，我们还使用 NetworkProfiler 预测 EMT 依赖性主调节剂，这些调节剂增强细胞黏附、迁移、侵袭和转移。为了进一步评估 NetworkProfiler 的性能，我们从 E-钙粘蛋白的候选调节剂列表中选择了 Krüppel 样因子 5（KLF5），并进行了体外验证实验。结果发现，siRNA 敲低 KLF5 显著降低了 E-钙粘蛋白的表达，并诱导了 EMT 特征性的形态变化。此外，新型候选 EMT 相关 microRNA miR-100 的体外实验证实了 miR-100 参与了几个 EMT 相关方面，这与 NetworkProfiler 的预测结果一致。
