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Roth P.,University of Zurich | Wischhusen J.,University of Wurzburg | Happold C.,University of Zurich | Chandran P.A.,University of Wurzburg | And 6 more authors.
Journal of Neurochemistry | Year: 2011

The prognosis of patients afflicted by glioblastoma remains poor. Biomarkers for the disease would be desirable in order to allow for an early detection of tumor progression or to indicate rapidly growing tumor subtypes requiring more intensive therapy. In this study, we investigated whether a blood-derived specific miRNA fingerprint can be defined in patients with glioblastoma. To this end, miRNA profiles from the blood of 20 patients with glioblastoma and 20 age- and sex-matched healthy controls were compared. Of 1158 tested miRNAs, 52 were significantly deregulated, as assessed by unadjusted Student′s t-test at an alpha level of 0.05. Of these, two candidates, miR-128 (up-regulated) and miR-342-3p (down-regulated), remained significant after correcting for multiple testing by Benjamini-Hochberg adjustment with a p-value of 0.025. The altered expression of these two biomarkers was confirmed in a second cohort of glioblastoma patients and healthy controls by real-time PCR and validated for patients who had received neither radio- nor chemotherapy and for patients who had their glioblastomas resected more than 6 months ago. Moreover, using machine learning, a comprehensive miRNA signature was obtained that allowed for the discrimination between blood samples of glioblastoma patients and healthy controls with an accuracy of 81% [95% confidence interval (CI) 78-84%], specificity of 79% (95% CI 75-83%) and sensitivity of 83% (95% CI 71-85%). In summary, our proof-of-concept study demonstrates that blood-derived glioblastoma-associated characteristic miRNA fingerprints may be suitable biomarkers and warrant further exploration. © 2011 International Society for Neurochemistry.

Kayvanpour E.,University of Heidelberg | Kayvanpour E.,German Center for Cardiovascular Research | Mansi T.,Siemens AG | Sedaghat-Hamedani F.,University of Heidelberg | And 26 more authors.
PLoS ONE | Year: 2015

Background: Despite modern pharmacotherapy and advanced implantable cardiac devices, overall prognosis and quality of life of HF patients remain poor. This is in part due to insufficient patient stratification and lack of individualized therapy planning, resulting in less effective treatments and a significant number of non-responders. Methods and Results: State-of-the-art clinical phenotyping was acquired, including magnetic resonance imaging (MRI) and biomarker assessment. An individualized, multi-scale model of heart function covering cardiac anatomy, electrophysiology, biomechanics and hemodynamics was estimated using a robust framework. The model was computed on n=46 HF patients, showing for the first time that advanced multi-scale models can be fitted consistently on large cohorts. Novel multi-scale parameters derived from the model of all cases were analyzed and compared against clinical parameters, cardiac imaging, lab tests and survival scores to evaluate the explicative power of the model and its potential for better patient stratification. Model validation was pursued by comparing clinical parameters that were not used in the fitting process against model parameters. Conclusion: This paper illustrates how advanced multi-scale models can complement cardiovascular imaging and how they could be applied in patient care. Based on obtained results, it becomes conceivable that, after thorough validation, such heart failure models could be applied for patient management and therapy planning in the future, as we illustrate in one patient of our cohort who received CRT-D implantation. Copyright: © 2015 Kayvanpour et al.

Lange J.,Febit group | Leidinger P.,Saarland University | Oehler T.,Febit group | Keller A.,Febit group | And 2 more authors.
Geburtshilfe und Frauenheilkunde | Year: 2010

The value of biomarkers for diagnostic and prognostic assessments has become well established in medicine. Technological advances, especially in genomics and transcriptomics, allow researchers to obtain molecular fingerprints of human diseases such as cancers, and fine-tune patient classification according to the detected molecular changes. MicroRNAs (miRNAs) are endogenous noncoding small RNAs negatively regulating the translation of coding messenger RNAs (mRNAs) in a sequence-specific manner. The role of mi-RNAs in pathogenesis and the power to associate expression changes with disease states underscores their value as molecular biomarkers. Automated miRNA biomarker profiling using febit's microfluidic microarray technology is a promising approach for diagnostic tests, permitting highly sensitive analysis to be performed even with limited clinical sample materials such as blood or other body fluids. Testing for marker miRNAs, for example in breast or ovarial cancer, using established biochips contributes to the development of predictive miRNA signatures for the accurate diagnosis, monitoring or prognosis of diseases. This has been proven in research studies where blood-based miRNA profiling on febit?s Geniom RT Analyzer resulted in accurate classification of patients. The technology presented here provides the basis for early diagnostic testing, detection and assessment of disease progression, all of which are essential for successful disease management, especially in tumor patients where timely therapeutic interventions are extremely critical. © Georg Thieme Verlag KG Stuttgart.

Leidinger P.,Saarland University | Keller A.,Febit Biomedical GmbH | Keller A.,Biomarker Discovery Center Heidelberg | Borries A.,Febit Biomedical GmbH | And 5 more authors.
BMC Cancer | Year: 2010

Background: MicroRNA (miRNA) signatures are not only found in cancer tissue but also in blood of cancer patients. Specifically, miRNA detection in blood offers the prospect of a non-invasive analysis tool.Methods: Using a microarray based approach we screened almost 900 human miRNAs to detect miRNAs that are deregulated in their expression in blood cells of melanoma patients. We analyzed 55 blood samples, including 20 samples of healthy individuals, 24 samples of melanoma patients as test set, and 11 samples of melanoma patients as independent validation set.Results: A hypothesis test based approch detected 51 differentially regulated miRNAs, including 21 miRNAs that were downregulated in blood cells of melanoma patients and 30 miRNAs that were upregulated in blood cells of melanoma patients as compared to blood cells of healthy controls. The tets set and the independent validation set of the melanoma samples showed a high correlation of fold changes (0.81). Applying hierarchical clustering and principal component analysis we found that blood samples of melanoma patients and healthy individuals can be well differentiated from each other based on miRNA expression analysis. Using a subset of 16 significant deregulated miRNAs, we were able to reach a classification accuracy of 97.4%, a specificity of 95% and a sensitivity of 98.9% by supervised analysis. MiRNA microarray data were validated by qRT-PCR.Conclusions: Our study provides strong evidence for miRNA expression signatures of blood cells as useful biomarkers for melanoma. © 2010 Leidinger et al; licensee BioMed Central Ltd.

Meder B.,University of Heidelberg | Keller A.,Biomarker Discovery Center Heidelberg | Vogel B.,University of Heidelberg | Haas J.,University of Heidelberg | And 9 more authors.
Basic Research in Cardiology | Year: 2011

MicroRNAs (miRNAs) are important regulators of adaptive and maladaptive responses in cardiovascular diseases and hence are considered to be potential therapeutical targets. However, their role as novel biomarkers for the diagnosis of cardiovascular diseases still needs to be systematically evaluated. We assessed here for the first time whole-genome miRNA expression in peripheral total blood samples of patients with acute myocardial infarction (AMI). We identified 121 miRNAs, which are significantly dysregulated in AMI patients in comparison to healthy controls. Among these, miR-1291 and miR-663b show the highest sensitivity and specificity for the discrimination of cases from controls. Using a novel self-learning pattern recognition algorithm, we identified a unique signature of 20 miRNAs that predicts AMI with even higher power (specificity 96%, sensitivity 90%, and accuracy 93%). In addition, we show that miR-30c and miR-145 levels correlate with infarct sizes estimated by Troponin T release. The here presented study shows that single miRNAs and especially miRNA signatures derived from peripheral blood, could be valuable novel biomarkers for cardiovascular diseases. © 2010 Springer-Verlag.

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