Dataset Information

Stability Assessment of Candidate Reference Genes in Urine Sediment of Prostate Cancer Patients for miRNA Applications.

ABSTRACT: We aimed at assessing the stability of candidate reference genes in urine sediments of men subjected to digital rectal examination for suspected prostate cancer (PCa). Two microRNAs (miR-191 and miR-25) and 1 small nucleolar RNA (SNORD48) were assayed in 35 post-DRE urine sediments of men with PCa and in 26 subjects with histologically confirmed benign prostatic hyperplasia (BPH). The stability of candidate reference genes was assessed through BestKeeper algorithm and equivalence test. miR-200b and miR-452 were used to test for the effect of normalization on target genes. Our results proved miR-191 to be the most stable gene, showing the lowest degree of variation and the highest stability value. miR-25 and SNORD48 values fell beyond the cutoff of acceptability. In conclusion, we recommend the use of miR-191 for normalization purposes in post-DRE urine sediments.

SUBMITTER: Egidi MG

PROVIDER: S-EPMC4452852 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature

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Stability Assessment of Candidate Reference Genes in Urine Sediment of Prostate Cancer Patients for miRNA Applications.

Egidi Maria Giulia MG Cochetti Giovanni G Guelfi Gabriella G Zampini Danilo D Diverio Silvana S Poli Giulia G Mearini Ettore E

Disease markers 20150520

We aimed at assessing the stability of candidate reference genes in urine sediments of men subjected to digital rectal examination for suspected prostate cancer (PCa). Two microRNAs (miR-191 and miR-25) and 1 small nucleolar RNA (SNORD48) were assayed in 35 post-DRE urine sediments of men with PCa and in 26 subjects with histologically confirmed benign prostatic hyperplasia (BPH). The stability of candidate reference genes was assessed through BestKeeper algorithm and equivalence test. miR-200b ...[more]

PMID: 26078486

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Project description:ImportanceUrine sediment microscopy is commonly performed during the evaluation of kidney disease. Interobserver reliability of nephrologists' urine sediment examination has not been well studied.ObjectiveAssess interobserver reliability of the urine sediment examination.Design, setting, and participantsIn this diagnostic test study, urine samples were prospectively collected from a convenience sample of adult patients from an academic hospital in the United States undergoing kidney biopsy from July 11, 2018, to March 20, 2019. Digital images and videos of urine sediment findings were captured using a bright-field microscope. These images and videos along with urine dipstick results were incorporated in online surveys and sent to expert nephrologists at 15 US teaching hospitals. They were asked to identify individual sediment findings and the most likely underlying disease process.ExposuresUrine dipstick results and urine sediment images from patients undergoing native kidney biopsy.Main outcomes and measuresInterobserver reliability of urine sediment microscopy findings estimated by overall percent agreement and Fleiss κ coefficients. Secondary outcomes included concordance of diagnoses suspected by nephrologists with corresponding kidney biopsy results.ResultsIn total, 10 surveys from 10 patients containing 76 study questions on individual features were sent to 21 nephrologists, 14 (67%) of whom completed them all. Their combined 1064 responses were analyzed. Overall percent agreement for casts was an estimated 59% (95% CI, 50%-69%), κ = 0.52 (95% CI, 0.42-0.62). For other sediment findings, overall percent agreement was an estimated 69% (95% CI, 61%-77%), κ = 0.65 (95% CI, 0.56-0.73). The κ estimates ranged from 0.13 (95% CI, 0.10-0.17) for mixed cellular casts to 0.90 (95% CI, 0.87-0.94) for squamous epithelial cells.Conclusions and relevanceIn this study, substantial variability occurred in the interpretation of urine sediment findings, even among expert nephrologists. Educational or technological innovations may help improve the urine sediment as a diagnostic tool.

Project description:BackgroundMicroRNAs (miRNAs) are a class of non-coding regulatory RNAs approximately 22 nucleotides in length that play a role in a wide range of biological processes. Abnormal miRNA function has been implicated in various human cancers including prostate cancer (PCa). Altered miRNA expression may serve as a biomarker for cancer diagnosis and treatment. However, limited data are available on the role of cancer-specific miRNAs. Integrative computational bioinformatics approaches are effective for the detection of potential outlier miRNAs in cancer.MethodsThe human miRNA-mRNA target network was reconstructed by integrating multiple miRNA-mRNA interaction datasets. Paired miRNA and mRNA expression profiling data in PCa versus benign prostate tissue samples were used as another source of information. These datasets were analyzed with an integrated bioinformatics framework to identify potential PCa miRNA signatures. In vitro q-PCR experiments and further systematic analysis were used to validate these prediction results.ResultsUsing this bioinformatics framework, we identified 39 miRNAs as potential PCa miRNA signatures. Among these miRNAs, 20 had previously been identified as PCa aberrant miRNAs by low-throughput methods, and 16 were shown to be deregulated in other cancers. In vitro q-PCR experiments verified the accuracy of these predictions. miR-648 was identified as a novel candidate PCa miRNA biomarker. Further functional and pathway enrichment analysis confirmed the association of the identified miRNAs with PCa progression.ConclusionsOur analysis revealed the scale-free features of the human miRNA-mRNA interaction network and showed the distinctive topological features of existing cancer miRNA biomarkers from previously published studies. A novel cancer miRNA biomarker prediction framework was designed based on these observations and applied to prostate cancer study. This method could be applied for miRNA biomarker prediction in other cancers.

Project description:BackgroundTMPRSS2:ERG (T2:ERG) and prostate cancer antigen 3 (PCA3) are the most advanced urine-based prostate cancer (PCa) early detection biomarkers.ObjectiveValidate logistic regression models, termed Mi-Prostate Score (MiPS), that incorporate serum prostate-specific antigen (PSA; or the multivariate Prostate Cancer Prevention Trial risk calculator version 1.0 [PCPTrc]) and urine T2:ERG and PCA3 scores for predicting PCa and high-grade PCa on biopsy.Design, setting, and participantsT2:ERG and PCA3 scores were generated using clinical-grade transcription-mediated amplification assays. Pretrained MiPS models were applied to a validation cohort of whole urine samples prospectively collected after digital rectal examination from 1244 men presenting for biopsy.Outcome measurements and statistical analysisArea under the curve (AUC) was used to compare the performance of serum PSA (or the PCPTrc) alone and MiPS models. Decision curve analysis (DCA) was used to assess clinical benefit.Results and limitationsAmong informative validation cohort samples (n=1225 [98%], 80% from patients presenting for initial biopsy), models incorporating T2:ERG had significantly greater AUC than PSA (or PCPTrc) for predicting PCa (PSA: 0.693 vs 0.585; PCPTrc: 0.718 vs 0.639; both p<0.001) or high-grade (Gleason score >6) PCa on biopsy (PSA: 0.729 vs 0.651, p<0.001; PCPTrc: 0.754 vs 0.707, p=0.006). MiPS models incorporating T2:ERG score had significantly greater AUC (all p<0.001) than models incorporating only PCA3 plus PSA (or PCPTrc or high-grade cancer PCPTrc [PCPThg]). DCA demonstrated net benefit of the MiPS_PCPTrc (or MiPS_PCPThg) model compared with the PCPTrc (or PCPThg) across relevant threshold probabilities.ConclusionsIncorporating urine T2:ERG and PCA3 scores improves the performance of serum PSA (or PCPTrc) for predicting PCa and high-grade PCa on biopsy.Patient summaryIncorporation of two prostate cancer (PCa)-specific biomarkers (TMPRSS2:ERG and PCA3) measured in the urine improved on serum prostate-specific antigen (or a multivariate risk calculator) for predicting the presence of PCa and high-grade PCa on biopsy. A combined test, Mi-Prostate Score, uses models validated in this study and is clinically available to provide individualized risk estimates.

Dataset Information

Stability Assessment of Candidate Reference Genes in Urine Sediment of Prostate Cancer Patients for miRNA Applications.

Publications

Stability Assessment of Candidate Reference Genes in Urine Sediment of Prostate Cancer Patients for miRNA Applications.

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