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Urinary Volatile Organic Compounds for the Detection of Prostate Cancer

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Listed:
  • Tanzeela Khalid
  • Raphael Aggio
  • Paul White
  • Ben De Lacy Costello
  • Raj Persad
  • Huda Al-Kateb
  • Peter Jones
  • Chris S Probert
  • Norman Ratcliffe

Abstract

The aim of this work was to investigate volatile organic compounds (VOCs) emanating from urine samples to determine whether they can be used to classify samples into those from prostate cancer and non-cancer groups. Participants were men referred for a trans-rectal ultrasound-guided prostate biopsy because of an elevated prostate specific antigen (PSA) level or abnormal findings on digital rectal examination. Urine samples were collected from patients with prostate cancer (n = 59) and cancer-free controls (n = 43), on the day of their biopsy, prior to their procedure. VOCs from the headspace of basified urine samples were extracted using solid-phase micro-extraction and analysed by gas chromatography/mass spectrometry. Classifiers were developed using Random Forest (RF) and Linear Discriminant Analysis (LDA) classification techniques. PSA alone had an accuracy of 62–64% in these samples. A model based on 4 VOCs, 2,6-dimethyl-7-octen-2-ol, pentanal, 3-octanone, and 2-octanone, was marginally more accurate 63–65%. When combined, PSA level and these four VOCs had mean accuracies of 74% and 65%, using RF and LDA, respectively. With repeated double cross-validation, the mean accuracies fell to 71% and 65%, using RF and LDA, respectively. Results from VOC profiling of urine headspace are encouraging and suggest that there are other metabolomic avenues worth exploring which could help improve the stratification of men at risk of prostate cancer. This study also adds to our knowledge on the profile of compounds found in basified urine, from controls and cancer patients, which is useful information for future studies comparing the urine from patients with other disease states.

Suggested Citation

  • Tanzeela Khalid & Raphael Aggio & Paul White & Ben De Lacy Costello & Raj Persad & Huda Al-Kateb & Peter Jones & Chris S Probert & Norman Ratcliffe, 2015. "Urinary Volatile Organic Compounds for the Detection of Prostate Cancer," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-15, November.
    • Handle: RePEc:plo:pone00:0143283
    DOI: 10.1371/journal.pone.0143283
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    1. Arun Sreekumar & Laila M. Poisson & Thekkelnaycke M. Rajendiran & Amjad P. Khan & Qi Cao & Jindan Yu & Bharathi Laxman & Rohit Mehra & Robert J. Lonigro & Yong Li & Mukesh K. Nyati & Aarif Ahsan & Sha, 2009. "Metabolomic profiles delineate potential role for sarcosine in prostate cancer progression," Nature, Nature, vol. 457(7231), pages 910-914, February.
    2. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    3. Kursa, Miron B. & Rudnicki, Witold R., 2010. "Feature Selection with the Boruta Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 36(i11).
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