A Novel Network Profiling Analysis Reveals System Changes in Epithelial-Mesenchymal Transition

Human Genome Center, Institute of Medical Science, University of Tokyo, Minato-ku, Tokyo, Japan.
PLoS ONE (Impact Factor: 3.53). 06/2011; 6(6):e20804. DOI: 10.1371/journal.pone.0020804
Source: PubMed

ABSTRACT 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.

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    ABSTRACT: Background Few studies have investigated prognostic biomarkers of distant metastases of lung cancer. One of the central difficulties in identifying biomarkers from microarray data is the availability of only a small number of samples, which results overtraining. Recently obtained evidence reveals that epithelial–mesenchymal transition (EMT) of tumor cells causes metastasis, which is detrimental to patients’ survival. Results This work proposes a novel optimization approach to discovering EMT-related prognostic biomarkers to predict the distant metastasis of lung cancer using both microarray and survival data. This weighted objective function maximizes both the accuracy of prediction of distant metastasis and the area between the disease-free survival curves of the non-distant and distant metastases. Seventy-eight patients with lung cancer and a follow-up time of 120 months are used to identify a set of gene markers and an independent cohort of 26 patients is used to evaluate the identified biomarkers. The medical records of the 78 patients show a significant difference between the disease-free survival times of the 37 non-distant- and the 41 distant-metastasis patients. The experimental results thus obtained are as follows. 1) The use of disease-free survival curves can compensate for the shortcoming of insufficient samples and greatly increase the test accuracy by 11.10%; and 2) the support vector machine with a set of 17 transcripts, such as CCL16 and CDKN2AIP, can yield a leave-one-out cross-validation accuracy of 93.59%, a test accuracy of 76.92%, a large disease-free survival area of 74.81%, and a mean survival prediction error of 3.99 months. The identified putative biomarkers are examined using related studies and signaling pathways to reveal the potential effectiveness of the biomarkers in prospective confirmatory studies. Conclusions The proposed new optimization approach to identifying prognostic biomarkers by combining multiple sources of data (microarray and survival) can facilitate the accurate selection of biomarkers that are most relevant to the disease while solving the problem of insufficient samples. Electronic supplementary material The online version of this article (doi:10.1186/s12859-015-0463-x) contains supplementary material, which is available to authorized users.
    BMC Bioinformatics 12/2015; 16(1). DOI:10.1186/s12859-015-0463-x · 2.67 Impact Factor
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    ABSTRACT: Krüppel-like factors (KLFs) comprise a highly conserved family of zinc finger transcription factors, that are involved in a plethora of cellular processes, ranging from proliferation and apoptosis to differentiation, migration and pluripotency. During the last few years, evidence on their role and deregulation in different human cancers has been emerging. This review will discuss current knowledge on Krüppel-like transcription in the epithelial-mesenchymal transition (EMT), invasion and metastasis, with a focus on epithelial cancer biology and the extensive interface with pluripotency. Furthermore, as KLFs are able to mediate different outcomes, important influences of the cellular and microenvironmental context will be highlighted. Finally, we attempt to integrate diverse findings on KLF functions in EMT and stem cell biology to fit in the current model of cellular plasticity as a tool for successful metastatic dissemination.
    Oncotarget 11/2013; 5(1). · 6.63 Impact Factor
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    ABSTRACT: Evidence in literature has demonstrated that some microRNAs (miRNAs) play a pivotal role in most solid tumor metastasis. Previous studies have showed that miR-100 is downregulated in human prostate cancer tissue compared to normal prostate and also significantly decreased in bone metastatic prostate cancer samples compared with primary prostate cancer. Argonaute 2 (AGO2) is the core effector protein of the miRNA-induced silencing complex and overexpression of AGO2 might enhance tumor metastasis. However, it is unknown whether and how miR-100 and AGO2 regulates metastasis of prostate cancer. Here, we report that miR-100 negatively regulated migration, invasion, epithelial-mesenchymal transition (EMT), colony formation, spheroid formation and expression of the stemness factors c-Myc, Oct4 and Klf4 in PC-3 and DU145 cells. Furthermore, miR-100 expression was negatively correlated with bone metastasis of prostate cancer patients. Notably, luciferase assay showed that AGO2 was a direct target of miR-100. Downregulation of AGO2 repressed migration, invasion, EMT and stemness of prostate cancer cells, and reversed the effects seen with miR-100 downregulation. Downregulation of AGO2 enhanced expression of miR-34a and miR-125b which can suppress migration, invasion, EMT and stemness of cancer cells. Taken together, our findings indicate that loss of miR-100 promotes the metastatic ability of prostate cancer cells at least partially by upregulating AGO2 expression through modulating migration, invasion, EMT and stemness of cancer cells, and suggest that miR-100/AGO2 may play an important role in regulating the metastasis of prostate cancer and is a potential target of prevention and therapy.
    International Journal of Oncology 04/2014; 45(1). DOI:10.3892/ijo.2014.2413 · 2.77 Impact Factor

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