Dataset Information

Bioinformatic Studies to Predict MicroRNAs with the Potential of Uncoupling RECK Expression from Epithelial-Mesenchymal Transition in Cancer Cells.

ABSTRACT: RECK is downregulated in many tumors, and forced RECK expression in tumor cells often results in suppression of malignant phenotypes. Recent findings suggest that RECK is upregulated after epithelial-mesenchymal transition (EMT) in normal epithelium-derived cells but not in cancer cells. Since several microRNAs (miRs) are known to target RECK mRNA, we hypothesized that certain miR(s) may be involved in this suppression of RECK upregulation after EMT in cancer cells. To test this hypothesis, we used three approaches: (1) text mining to find miRs relevant to EMT in cancer cells, (2) predicting miR targets using four algorithms, and (3) comparing miR-seq data and RECK mRNA data using a novel non-parametric method. These approaches identified the miR-183-96-182 cluster as a strong candidate. We also looked for transcription factors and signaling molecules that may promote cancer EMT, miR-183-96-182 upregulation, and RECK downregulation. Here we describe our methods, findings, and a testable hypothesis on how RECK expression could be regulated in cancer cells after EMT.

SUBMITTER: Wang Z

PROVIDER: S-EPMC4874744 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Publications

Bioinformatic Studies to Predict MicroRNAs with the Potential of Uncoupling RECK Expression from Epithelial-Mesenchymal Transition in Cancer Cells.

Wang Zhipeng Z Murakami Ryusuke R Yuki Kanako K Yoshida Yoko Y Noda Makoto M

Cancer informatics 20160519

RECK is downregulated in many tumors, and forced RECK expression in tumor cells often results in suppression of malignant phenotypes. Recent findings suggest that RECK is upregulated after epithelial-mesenchymal transition (EMT) in normal epithelium-derived cells but not in cancer cells. Since several microRNAs (miRs) are known to target RECK mRNA, we hypothesized that certain miR(s) may be involved in this suppression of RECK upregulation after EMT in cancer cells. To test this hypothesis, we u ...[more]

PMID: 27226706

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Project description:Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive carcinoma entities worldwide with early and rapid dissemination. Recently, we discussed the role of microRNAs as epigenetic regulators of Epithelial-to-Mesenchymal Transition (EMT) in PDAC. In this study, we investigated their value as diagnostic and prognostic markers in tissue and blood samples of 185 patients including PDAC, non-malignant pancreatic disorders, and age-matched healthy controls. Expression of the microRNA-200-family (microRNAs -141, -200a, -200b, -200c, -429) and microRNA-148a was significantly downregulated in tissue of PDAC Union internationale contre le cancer (UICC) Stage II. Correspondingly, stromal PDAC tissue showed strong expression of Fibronectin, Vimentin, and ZEB-1 (Zinc finger E-box-binding homeobox) versus low expression of E-cadherin. Transient transfection of microRNA-200b and microRNA-200c mimics resulted in the downregulation of their key target ZEB-1. Inversely, blood serum analyses of patients with PDAC UICC Stages II, III, and IV showed a significant over-expression of microRNA-200-family members, microRNA-148a, microRNA-10b, and microRNA-34a. Correspondingly, Enzyme-linked Immunosorbent Assay (ELISA) analyses revealed a significant over-expression of soluble E-cadherin in serum samples of PDAC patients versus healthy controls. The best diagnostic accuracy to distinguish between PDAC and non-PDAC in this patient collective could be achieved in tissue by microRNA-148a with an area under the receiver-operating-characteristic (ROC) curve (AUC) of 0.885 and in blood serum by a panel of microRNA-141, -200b, -200c, and CA.19-9 with an AUC of 0.890. Both diagnostic tools outreach the diagnostic performance of the currently most common diagnostic biomarker CA.19-9 (AUC of 0.834). Kaplan Meier survival analysis of this patient collective revealed an improved overall survival in PDAC patients with high expression of tissue-related microRNA-34a, -141, -200b, -200c, and -429. In conclusion, EMT-regulating microRNAs have great potential as liquid and solid biopsy markers in PDAC patients. Their prognostic and therapeutic benefits remain important tasks for future studies.

Project description:Epithelial-mesenchymal transition (EMT) is a fundamental biological process that plays a central role in embryonic development, tissue regeneration, and cancer metastasis. Transforming growth factor-β (TGFβ) is a potent inducer of this cellular transition, which is composed of transitions from an epithelial state to intermediate or partial EMT state(s) to a mesenchymal state. Using computational models to predict cell state transitions in a specific experiment is inherently difficult for reasons including model parameter uncertainty and error associated with experimental observations. In this study, we demonstrate that a data-assimilation approach using an ensemble Kalman filter, which combines limited noisy observations with predictions from a computational model of TGFβ-induced EMT, can reconstruct the cell state and predict the timing of state transitions. We used our approach in proof-of-concept "synthetic" in silico experiments, in which experimental observations were produced from a known computational model with the addition of noise. We mimic parameter uncertainty in in vitro experiments by incorporating model error that shifts the TGFβ doses associated with the state transitions and reproduces experimentally observed variability in cell state by either shifting a single parameter or generating "populations" of model parameters. We performed synthetic experiments for a wide range of TGFβ doses, investigating different cell steady-state conditions, and conducted parameter studies varying properties of the data-assimilation approach including the time interval between observations and incorporating multiplicative inflation, a technique to compensate for underestimation of the model uncertainty and mitigate the influence of model error. We find that cell state can be successfully reconstructed and the future cell state predicted in synthetic experiments, even in the setting of model error, when experimental observations are performed at a sufficiently short time interval and incorporate multiplicative inflation. Our study demonstrates the feasibility and utility of a data-assimilation approach to forecasting the fate of cells undergoing EMT.

Project description:Epithelial-mesenchymal transition (EMT) is a biological process that plays a central role in embryonic development, tissue regeneration, and cancer metastasis. Transforming growth factor-β (TGFβ) is a potent inducer of this cellular transition, comprising transitions from an epithelial state to partial or hybrid EMT state(s), to a mesenchymal state. Recent experimental studies have shown that, within a population of epithelial cells, heterogeneous phenotypical profiles arise in response to different time- and TGFβ dose-dependent stimuli. This offers a challenge for computational models, as most model parameters are generally obtained to represent typical cell responses, not necessarily specific responses nor to capture population variability. In this study, we applied a data-assimilation approach that combines limited noisy observations with predictions from a computational model, paired with parameter estimation. Synthetic experiments mimic the biological heterogeneity in cell states that is observed in epithelial cell populations by generating a large population of model parameter sets. Analysis of the parameters for virtual epithelial cells with biologically significant characteristics (e.g., EMT prone or resistant) illustrates that these sub-populations have identifiable critical model parameters. We perform a series of in silico experiments in which a forecasting system reconstructs the EMT dynamics of each virtual cell within a heterogeneous population exposed to time-dependent exogenous TGFβ dose and either an EMT-suppressing or EMT-promoting perturbation. We find that estimating population-specific critical parameters significantly improved the prediction accuracy of cell responses. Thus, with appropriate protocol design, we demonstrate that a data-assimilation approach successfully reconstructs and predicts the dynamics of a heterogeneous virtual epithelial cell population in the presence of physiological model error and parameter uncertainty.

Dataset Information

Bioinformatic Studies to Predict MicroRNAs with the Potential of Uncoupling RECK Expression from Epithelial-Mesenchymal Transition in Cancer Cells.

Publications

Bioinformatic Studies to Predict MicroRNAs with the Potential of Uncoupling RECK Expression from Epithelial-Mesenchymal Transition in Cancer Cells.

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