Dataset Information

Identification of a biomarker panel for improvement of prostate cancer diagnosis by volatile metabolic profiling of urine.

ABSTRACT:

Background

The lack of sensitive and specific biomarkers for the early detection of prostate cancer (PCa) is a major hurdle to improve patient management.

Methods

A metabolomics approach based on GC-MS was used to investigate the performance of volatile organic compounds (VOCs) in general and, more specifically, volatile carbonyl compounds (VCCs) present in urine as potential markers for PCa detection.

Results

Results showed that PCa patients (n = 40) can be differentiated from cancer-free subjects (n = 42) based on their urinary volatile profile in both VOCs and VCCs models, unveiling significant differences in the levels of several metabolites. The models constructed were further validated using an external validation set (n = 18 PCa and n = 18 controls) to evaluate sensitivity, specificity and accuracy of the urinary volatile profile to discriminate PCa from controls. The VOCs model disclosed 78% sensitivity, 94% specificity and 86% accuracy, whereas the VCCs model achieved the same sensitivity, a specificity of 100% and an accuracy of 89%. Our findings unveil a panel of 6 volatile compounds significantly altered in PCa patients' urine samples that was able to identify PCa, with a sensitivity of 89%, specificity of 83%, and accuracy of 86%.

Conclusions

It is disclosed a biomarker panel with potential to be used as a non-invasive diagnostic tool for PCa.

SUBMITTER: Lima AR

PROVIDER: S-EPMC6889512 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundProstate cancer diagnosis using the PSA test remains controversial because of overdiagnosis and overtreatment of potentially indolent cancers. There remains a need to increase the diagnostic lead time and to target treatment to patients with significant disease. One possible approach to overcome the limitations of PSA is to screen men for the molecular signature of early PCA, monitor the rate of disease progression and target treatment to patients who are likely to benefit from it. Such an approach requires a large panel of markers that define a molecular clock for PCA. We recently developed a panel of 19 markers for the non-invasive detection of PCA from urine DNA. It raised the possibility that additional methylation markers could be successfully analyzed from urine DNA, a prerequisite for increasing the diagnostic lead time and enabling disease monitoring.MethodsWe developed semi-quantitative polymerase chain reaction assays for 13 additional markers and determined their methylation status in 150 urine DNAs from 94 patients with elevated PSA. Eighty five samples were obtained following DRE and 65 samples were from first void. We combined the data of the 13 new markers with the previously reported 19 markers and calculated the sensitivity, specificity, negative and positive predictive values at every threshold from one to 32 positive markers.ResultsUsing 10of32 positive markers as the threshold to recommend a biopsy yields a sensitivity of 81% (95% CI 0.68-0.93) and 93% (95% CI 0.84-1.02) and a specificity of 76% (95% CI 0.63-0.88) and 77% (95% CI 0.63-0.91) from DRE and FV DNA, respectively. The PPV was 71% and 77% and the NPV was 85% and 93% from DRE and FV, respectively.ConclusionsThis study shows that large marker panels can be analyzed from urine DNA without loss of sensitivity or specificity. Using 32 markers improved the stratification of patients undergoing screening for PCA particularly for patients below the 10of32 threshold. The results show the utility of larger biomarker panels for PCA diagnosis and suggest that the development of the panels needed to monitor disease progression could be successfully accomplished.