Background:The aim of this study was to investigate the hypothesis that changes in circulating microRNAs (miRs) represent potentially useful biomarkers for the diagnosis, staging and prediction of outcome in prostate cancer.Methods:Real-time polymerase chain reaction analysis of 742 miRs was performed using plasma-derived circulating microvesicles of 78 prostate cancer patients and 28 normal control individuals to identify differentially quantified miRs.Results:A total of 12 miRs were differentially quantified in prostate cancer patients compared with controls, including 9 in patients without metastases. In all, 11 miRs were present in significantly greater amounts in prostate cancer patients with metastases compared with those without metastases. The association of miR-141 and miR-375 with metastatic prostate cancer was confirmed using serum-derived exosomes and microvesicles in a separate cohort of patients with recurrent or non-recurrent disease following radical prostatectomy. An analysis of five selected miRs in urine samples found that miR-107 and miR-574-3p were quantified at significantly higher concentrations in the urine of men with prostate cancer compared with controls.Conclusion:These observations suggest that changes in miR concentration in prostate cancer patients may be identified by analysing various body fluids. Moreover, circulating miRs may be used to diagnose and stage prostate cancer.

BACKGROUND:Massively parallel DNA sequencing of cell-free fetal DNA from maternal blood can detect fetal chromosomal abnormalities. Although existing algorithms focus on the detection of fetal trisomy 21 (T21), these same algorithms have difficulty detecting trisomy 18 (T18).

BackgroundThe Gail model is widely used for the assessment of risk of invasive breast cancer based on recognized clinical risk factors. In recent years, a substantial number of single-nucleotide polymorphisms (SNPs) associated with breast cancer risk have been identified. However, it remains unclear how to effectively integrate clinical and genetic risk factors for risk assessment.MethodsSeven SNPs associated with breast cancer risk were selected from the literature and genotyped in white non-Hispanic women in a nested case–control cohort of 1664 case patients and 1636 control subjects within the Women’s Health Initiative Clinical Trial. SNP risk scores were computed based on previously published odds ratios assuming a multiplicative model. Combined risk scores were calculated by multiplying Gail risk estimates by the SNP risk scores. The independence of Gail risk and SNP risk was evaluated by logistic regression. Calibration of relative risks was evaluated using the Hosmer–Lemeshow test. The performance of the combined risk scores was evaluated using receiver operating characteristic curves. The net reclassification improvement (NRI) was used to assess improvement in classification of women into low (<1.5%), intermediate (1.5%–2%), and high (>2%) categories of 5-year risk. All tests of statistical significance were two-sided.ResultsThe SNP risk score was nearly independent of Gail risk. There was good agreement between predicted and observed SNP relative risks. In the analysis for receiver operating characteristic curves, the combined risk score was more discriminating, with area under the curve of 0.594 compared with area under the curve of 0.557 for Gail risk alone (P < .001). Classification also improved for 5.6% of case patients and 2.9% of control subjects, showing an NRI value of 0.085 (P = 1.0 × 10−5). Focusing on women with intermediate Gail risk resulted in an improved NRI of 0.195 (P = 8.6 × 10−5).ConclusionsCombining validated common genetic risk factors with clinical risk factors resulted in modest improvement in classification of breast cancer risks in white non-Hispanic postmenopausal women. Classification performance was further improved by focusing on women at intermediate risk.