References of "Merkle, Ruth"
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See detailTTCA: an R package for the identification of differentially expressed genes in time course microarray data
Albrecht, Marco UL; Stichel, Damian; Müller, Benedikt et al

in BMC Bioinformatics (2017), 18(1), 33

Background: The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes ... [more ▼]

Background: The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements. Results: The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF). Conclusion: Here we describe a new, efficient method for the analysis of sparse and heterogeneous time course data with high detection sensitivity and transparency. It is implemented as R package TTCA (transcript time course analysis) and can be installed from the Comprehensive R Archive Network, CRAN. The source code is provided with the Additional file 1. [less ▲]

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See detailDownregulation of the TGF-beta pseudoreceptor BAMBI in non-small cell lung cancer enhances TGF-beta signaling and invasion.
Marwitz, Sebastian; Depner, Sofia; Dvornikov, Dmytro et al

in Cancer research (2016)

Non-small cell lung cancer (NSCLC) is characterized by early metastasis and has the highest mortality rate among all solid tumors, with the majority of patients diagnosed at an advanced stage where ... [more ▼]

Non-small cell lung cancer (NSCLC) is characterized by early metastasis and has the highest mortality rate among all solid tumors, with the majority of patients diagnosed at an advanced stage where curative therapeutic options are lacking. In this study, we identify a targetable mechanism involving TGF-beta elevation that orchestrates tumor progression in this disease. Substantial activation of this pathway was detected in human lung cancer tissues with concomitant downregulation of BAMBI, a negative regulator of the TGF-beta signaling pathway. Alterations of epithelial-to-mesenchymal transition (EMT) marker expression were observed in lung cancer samples compared to tumor-free tissues. Distinct alterations in the DNA methylation of the gene regions encoding TGF-beta pathway components were detected in NSCLC samples compared to tumor-free lung tissues. In particular, epigenetic silencing of BAMBI was identified as a hallmark of NSCLC. Reconstitution of BAMBI expression in NSCLC cells resulted in a marked reduction of TGF-beta-induced EMT, migration and invasion in vitro, along with reduced tumor burden and tumor growth in vivo. In conclusion, our results demonstrate how BAMBI downregulation drives the invasiveness of NSCLC, highlighting TGF-beta signaling as a candidate therapeutic target in this setting. [less ▲]

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