Diagnostic and Prognostic MicroRNA Biomarkers for Prostate Cancer in Cell-free Urine

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Abstract

Widespread use of prostate-specific antigen (PSA) testing for prostate cancer (PC) detection has led to extensive overdiagnosis and overtreatment. Urine-based microRNA (miRNA) biomarkers could be useful in PC diagnosis and prognosis.

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pROC: an open-source package for R and S+ to analyze and compare ROC curves

Xavier Robin, Natacha Turck, Alexandre Hainard … (2013)

Background Receiver operating characteristic (ROC) curves are useful tools to evaluate classifiers in biomedical and bioinformatics applications. However, conclusions are often reached through inconsistent use or insufficient statistical analysis. To support researchers in their ROC curves analysis we developed pROC, a package for R and S+ that contains a set of tools displaying, analyzing, smoothing and comparing ROC curves in a user-friendly, object-oriented and flexible interface. Results With data previously imported into the R or S+ environment, the pROC package builds ROC curves and includes functions for computing confidence intervals, statistical tests for comparing total or partial area under the curve or the operating points of different classifiers, and methods for smoothing ROC curves. Intermediary and final results are visualised in user-friendly interfaces. A case study based on published clinical and biomarker data shows how to perform a typical ROC analysis with pROC. Conclusions pROC is a package for R and S+ specifically dedicated to ROC analysis. It proposes multiple statistical tests to compare ROC curves, and in particular partial areas under the curve, allowing proper ROC interpretation. pROC is available in two versions: in the R programming language or with a graphical user interface in the S+ statistical software. It is accessible at http://expasy.org/tools/pROC/ under the GNU General Public License. It is also distributed through the CRAN and CSAN public repositories, facilitating its installation.

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Regulation of microRNA biogenesis.

Minju Ha, V Kim (2014)

MicroRNAs (miRNAs) are small non-coding RNAs that function as guide molecules in RNA silencing. Targeting most protein-coding transcripts, miRNAs are involved in nearly all developmental and pathological processes in animals. The biogenesis of miRNAs is under tight temporal and spatial control, and their dysregulation is associated with many human diseases, particularly cancer. In animals, miRNAs are ∼22 nucleotides in length, and they are produced by two RNase III proteins--Drosha and Dicer. miRNA biogenesis is regulated at multiple levels, including at the level of miRNA transcription; its processing by Drosha and Dicer in the nucleus and cytoplasm, respectively; its modification by RNA editing, RNA methylation, uridylation and adenylation; Argonaute loading; and RNA decay. Non-canonical pathways for miRNA biogenesis, including those that are independent of Drosha or Dicer, are also emerging.

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Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets.

Claus Andersen, Jens Ledet Jensen, Torben Ørntoft (2004)

Accurate normalization is an absolute prerequisite for correct measurement of gene expression. For quantitative real-time reverse transcription-PCR (RT-PCR), the most commonly used normalization strategy involves standardization to a single constitutively expressed control gene. However, in recent years, it has become clear that no single gene is constitutively expressed in all cell types and under all experimental conditions, implying that the expression stability of the intended control gene has to be verified before each experiment. We outline a novel, innovative, and robust strategy to identify stably expressed genes among a set of candidate normalization genes. The strategy is rooted in a mathematical model of gene expression that enables estimation not only of the overall variation of the candidate normalization genes but also of the variation between sample subgroups of the sample set. Notably, the strategy provides a direct measure for the estimated expression variation, enabling the user to evaluate the systematic error introduced when using the gene. In a side-by-side comparison with a previously published strategy, our model-based approach performed in a more robust manner and showed less sensitivity toward coregulation of the candidate normalization genes. We used the model-based strategy to identify genes suited to normalize quantitative RT-PCR data from colon cancer and bladder cancer. These genes are UBC, GAPD, and TPT1 for the colon and HSPCB, TEGT, and ATP5B for the bladder. The presented strategy can be applied to evaluate the suitability of any normalization gene candidate in any kind of experimental design and should allow more reliable normalization of RT-PCR data.