Limited utility of qPCR-based detection of tumor-specific circulating mRNAs in whole blood from clear cell renal cell carcinoma patients

BACKGROUND: RNA sequencing data is providing abundant information about the levels of dysregulation of genes in various tumors. These data, as well as data based on older microarray technologies have enabled the identification of many genes which are upregulated in clear cell renal cell carcinoma (ccRCC) compared to matched normal tissue. Here we use RNA sequencing data in order to construct a panel of highly overexpressed genes in ccRCC so as to evaluate their RNA levels in whole blood and determine any diagnostic potential of these levels for renal cell carcinoma patients. METHODS: A bioinformatics analysis with Python was performed using TCGA, GEO and other databases to identify genes which are upregulated in ccRCC while being absent in the blood of healthy individuals. Quantitative Real Time PCR (RT-qPCR) was subsequently used to measure the levels of candidate genes in whole blood (PAX gene) of 16 ccRCC patients versus 11 healthy individuals. PCR results were processed in qBase and GraphPadPrism and statistics was done with Mann-Whitney U test. RESULTS: While most analyzed genes were either undetectable or did not show any dysregulated expression, two genes, CDK18 and CCND1, were paradoxically downregulated in the blood of ccRCC patients compared to healthy controls. Furthermore, LOX showed a tendency towards upregulation in metastatic ccRCC samples compared to non-metastatic. CONCLUSIONS: This analysis illustrates the difficulty of detecting tumor regulated genes in blood and the possible influence of interference from expression in blood cells even for genes conditionally absent in normal blood. Testing in plasma samples indicated that tumor specific mRNAs were not detectable. While CDK18, CCND1 and LOX mRNAs might carry biomarker potential, this would require validation in an independent, larger patient cohort

Identity development in adolescents with mental problems

Background: In the revision of the Diagnostic and Statistical Manual (DSM-5), "Identity" is an essential diagnostic criterion for personality disorders (self-related personality functioning) in the alternative approach to the diagnosis of personality disorders in Section III of DSM-5. Integrating a broad range of established identity concepts, AIDA (Assessment of Identity Development in Adolescence) is a new questionnaire to assess pathology-related identity development in healthy and disturbed adolescents aged 12 to 18 years. Aim of the present study is to investigate differences in identity development between adolescents with different psychiatric diagnoses. Methods: Participants were 86 adolescent psychiatric in- and outpatients aged 12 to 18 years. The test set includes the questionnaire AIDA and two semi-structured psychiatric interviews (SCID-II, K-DIPS). The patients were assigned to three diagnostic groups (personality disorders, internalizing disorders, externalizing disorders). Differences were analyzed by multivariate analysis of variance MANOVA. Results: In line with our hypotheses, patients with personality disorders showed the highest scores in all AIDA scales with T>70. Patients with externalizing disorders showed scores in an average range compared to population norms, while patients with internalizing disorders lay in between with scores around T=60. The AIDA total score was highly significant between the groups with a remarkable effect size of f= 0.44. Conclusion: Impairment of identity development differs between adolescent patients with different forms of mental disorders. The AIDA questionnaire is able to discriminate between these groups. This may help to improve assessment and treatment of adolescents with severe psychiatric problems

Utilization of ordinal response structures in classification with high-dimensional expression data

Molecular diagnosis or prediction of clinical treatment outcome based on high-throughput genomics data is a modern application of machine learning techniques for clinical problems. In practice, clinical parameters, such as patient health status or toxic reaction to therapy, are often measured on an ordinal scale (e.g. good, fair, poor). Commonly, the prediction of ordinal end-points is treated as a multi-class classification problem, disregarding the ordering information contained in the response. This may result in a loss of prediction accuracy. Classical approaches to model ordinal response directly, including for instance the cumulative logit model, are typically not applicable to high-dimensional data. We present hierarchical twoing (hi2), a novel algorithm for classification of high-dimensional data into ordered categories. hi2 combines the power of well-understood binary classification with ordinal response prediction. A comparison of several approaches for ordinal classification on real world data as well as simulated data shows that classification algorithms especially designed to handle ordered categories fail to improve upon state-of-the-art non-ordinal classification algorithms. In general, the classification performance of an algorithm is dominated by its ability to deal with the high-dimensionality of the data. Only hi2 outperforms its competitors in the case of moderate effects

Les Pissenlits des marais (Taraxacum sect. Palustria) en Hesse

7 Sumpflöwenzahn-Arten sind in Hessen nachgewiesen, wobei von Taraxacum bavaricum und T. pauckertianum nur historische Nachweise vorliegen. Taraxacum hollandicum ist am weitesten in Hessen verbreitet und konnte bei der zwischen 2002 und 2004 durchgeführten Untersuchung in 10 von 12 untersuchten Gebieten mit mehr als 35000 Exemplaren nachgewiesen werden. Taraxacum germanicum wurde bei Münzenberg, Selters und im Mönchbruch gefunden. Taraxacum multilepis und T. trilobifolium haben individuenarme Populationen im Naturschutzgebiet Ludwigsquelle beziehungsweise im Mönchbruch, auf der Rodwiese und bei Bieber. Taraxacum subalpinum ist mit 2 sehr kleinen Populationen in der Wieseckaue bei Gießen die seltenste hessische Sumpflöwenzahnart.Seven species of Taraxacum sect. Palustria are known to have occurred in Hesse, of which T. bavaricum and T. pauckertianum now appear to be extinct. T. hollandicum is the most widespread species, with more than 35,000 plants recorded in a survey conducted between 2002 and 2004 in 10 areas in the Wetterau district and in southern Hesse. T. germanicum was found near Muenzenberg, Selters, and Moenchbruch. A sparse population of T. multilepis was found in the Ludwigsquelle conservation area, and a sparse population of T. trilobifolium was found near Moenchbruch, Rodwiese, and Bieber. T. subalpinum is rare, with only two very small populations occurring in the Wieseck valley near Giessen.7 espèces de pissenlits des marais sont prouvées en Hesse dont 2 espèces (Taraxacum bavaricum, T. pauckertianum) ne sont connues que par l’histoire. Taraxacum hollandicum est le plus répandu en Hesse. Grâce aux recherches qui ont été effectuées entre 2002 et 2004, il a pu être prouvé plus de 35 000 exemplaires sur 10 des 12 aires examinées. Taraxacum germanicum a été trouvé près de Münzenberg, de Selters et dans le Mönchbruch. Taraxacum multilepis et T. trilobifolium ont de rares populations de quelques spécimens dans la réserve naturelle du Ludwigsquelle et dans le Mönchbruch, sur la Rodwiese et près de Bieber. Taraxacum subalpinum, la plus rare des espèces en Hesse, a deux très petites populations dans la zone de Wieseckaue près de Gießen

A Novel Predictor Tool of Biochemical Recurrence after Radical Prostatectomy Based on a Five-MicroRNA Tissue Signature

Within five to ten years after radical prostatectomy (RP), approximately 15-34% of prostate cancer (PCa) patients experience biochemical recurrence (BCR), which is defined as recurrence of serum levels of prostate-specific antigen >0.2 µg/L, indicating probable cancer recurrence. Models using clinicopathological variables for predicting this risk for patients lack accuracy. There is hope that new molecular biomarkers, like microRNAs (miRNAs), could be potential candidates to improve risk prediction. Therefore, we evaluated the BCR prognostic capability of 20 miRNAs, which were selected by a systematic literature review. MiRNA expressions were measured in formalin-fixed, paraffin-embedded (FFPE) tissue RP samples of 206 PCa patients by RT-qPCR. Univariate and multivariate Cox regression analyses were performed, to assess the independent prognostic potential of miRNAs. Internal validation was performed, using bootstrapping and the split-sample method. Five miRNAs (miR-30c-5p/31-5p/141-3p/148a-3p/miR-221-3p) were finally validated as independent prognostic biomarkers. Their prognostic ability and accuracy were evaluated using C-statistics of the obtained prognostic indices in the Cox regression, time-dependent receiver-operating characteristics, and decision curve analyses. Models of miRNAs, combined with relevant clinicopathological factors, were built. The five-miRNA-panel outperformed clinically established BCR scoring systems, while their combination significantly improved predictive power, based on clinicopathological factors alone. We conclude that this miRNA-based-predictor panel will be worth to be including in future studies

Influence of cyclosporin A on the respiration of isolated rat kidney mitochondria

AbstractIn vitro exposure of isolated rat kidney mitochondria to cyclosporin A, a new immunosuppressive agent with serious nephrotoxic side-effects, leads to alterations of both succinate- and glutamate plus malate-supported respiration in a dose-related manner. ADP- and 2,4-dinitrophenol-stimulated respiration, respiratory control indices, and ADP/0 ratios are decreased. The mitochondrial alterations are discussed as possible pathogenetic reasons of cyclosporin A nephrotoxicity.Cyclosporin A toxicityRat kidneyMitochondrion respiratio

Methodological comparison of agent models

Hybrid agent architectures comprise the radical change of paradigms in AI over the past decades by reconciling the different styles of reactive, deliberative, even social systems. They have been successfully applied to a range of complex real-world domains. Due to their originally informal background, a verification of design goals in derived implementations, theoretical foundations, and a detailed comparison with other agent models have not yet been obvious. The present work proposes a formal methodology to bridge the gap between theoretical and practical aspects especially of hybrid designs, such as the layered INTERRAP. The employed, connected stages of specification, i.e., architecture, computational model, theory, proof calculus, and implementation, also provide a yet unique framework for comparing heterogeneous agent models including unified and logic-based ones. Based on recent work on INTERRAP, we demonstrate that this methodology allows to compare state-of-the-art designs from robotics, AI, computer science, and cognitive science with respect to a spectrum of inherent properties along the two dimensions of abstraction and declarativity. This supports our claim that INTERRAP is a coherent and advanced account of layered agency including goal-oriented abstraction planning in on-line interaction with reactive skills and social reasoning. We also derive particular research issues to guide the future development of INTERRAP

Circular RNAs in Clear Cell Renal Cell Carcinoma: Their Microarray-Based Identification, Analytical Validation, and Potential Use in a Clinico-Genomic Model to Improve Prognostic Accuracy

Circular RNAs (circRNAs) may act as novel cancer biomarkers. However, a genome-wide evaluation of circRNAs in clear cell renal cell carcinoma (ccRCC) has yet to be conducted. Therefore, the objective of this study was to identify and validate circRNAs in ccRCC tissue with a focus to evaluate their potential as prognostic biomarkers. A genome-wide identification of circRNAs in total RNA extracted from ccRCC tissue samples was performed using microarray analysis. Three relevant differentially expressed circRNAs were selected (circEGLN3, circNOX4, and circRHOBTB3), their circular nature was experimentally confirmed, and their expression-along with that of their linear counterparts-was measured in 99 malignant and 85 adjacent normal tissue samples using specifically established RT-qPCR assays. The capacity of circRNAs to discriminate between malignant and adjacent normal tissue samples and their prognostic potential (with the endpoints cancer-specific, recurrence-free, and overall survival) after surgery were estimated by C-statistics, Kaplan-Meier method, univariate and multivariate Cox regression analysis, decision curve analysis, and Akaike and Bayesian information criteria. CircEGLN3 discriminated malignant from normal tissue with 97% accuracy. We generated a prognostic for the three endpoints by multivariate Cox regression analysis that included circEGLN3, circRHOBT3 and linRHOBTB3. The predictive outcome accuracy of the clinical models based on clinicopathological factors was improved in combination with this circRNA-based signature. Bootstrapping as well as Akaike and Bayesian information criteria confirmed the statistical significance and robustness of the combined models. Limitations of this study include its retrospective nature and the lack of external validation. The study demonstrated the promising potential of circRNAs as diagnostic and particularly prognostic biomarkers in ccRCC patients

Sequential interim analyses of survival data in DNA microarray experiments

<p>Abstract</p> <p>Background</p> <p>Discovery of biomarkers that are correlated with therapy response and thus with survival is an important goal of medical research on severe diseases, e.g. cancer. Frequently, microarray studies are performed to identify genes of which the expression levels in pretherapeutic tissue samples are correlated to survival times of patients. Typically, such a study can take several years until the full planned sample size is available.</p> <p>Therefore, interim analyses are desirable, offering the possibility of stopping the study earlier, or of performing additional laboratory experiments to validate the role of the detected genes. While many methods correcting the multiple testing bias introduced by interim analyses have been proposed for studies of one single feature, there are still open questions about interim analyses of multiple features, particularly of high-dimensional microarray data, where the number of features clearly exceeds the number of samples. Therefore, we examine false discovery rates and power rates in microarray experiments performed during interim analyses of survival studies. In addition, the early stopping based on interim results of such studies is evaluated. As stop criterion we employ the achieved average power rate, i.e. the proportion of detected true positives, for which a new estimator is derived and compared to existing estimators.</p> <p>Results</p> <p>In a simulation study, pre-specified levels of the false discovery rate are maintained in each interim analysis, where reduced levels as used in classical group sequential designs of one single feature are not necessary. Average power rates increase with each interim analysis, and many studies can be stopped prior to their planned end when a certain pre-specified power rate is achieved. The new estimator for the power rate slightly deviates from the true power rate but is comparable to other estimators.</p> <p>Conclusions</p> <p>Interim analyses of microarray experiments can provide evidence for early stopping of long-term survival studies. The developed simulation framework, which we also offer as a new R package 'SurvGenesInterim' available at <url>http://survgenesinter.R-Forge.R-Project.org</url>, can be used for sample size planning of the evaluated study design.</p