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    Clin Cancer Res. 2007 Mar 15;13(6):1720-5.

    Molecular detection of localized prostate cancer using quantitative methylation-specific PCR on urinary cells obtained following prostate massage.

    Source

    Institute for Cancer Studies and Academic Urology Unit, University of Sheffield, Royal Hallamshire Hospital, Sheffield, UK. mroupret@club-internet.fr

    Abstract

    PURPOSE:

    The diagnosis of localized prostate cancer is difficult due to a lack of cancer-specific biomarkers. Many patients require repeat prostate biopsies to diagnose the disease. We investigated whether aberrant promoter hypermethylation in prostatic fluid could reliably detect prostate cancer.

    EXPERIMENTAL DESIGN:

    Urine samples were collected after prostate massage from 95 patients with localized prostate cancer undergoing radical prostatectomy (63 pT(1), 31 pT(2), and 1 pT(3)) and from 38 control patients. Ten genes (GSTP1, RASSF1a, ECDH1, APC, DAPK, MGMT, p14, p16, RARbeta2, and TIMP3) were investigated using quantitative real-time methylation-specific PCR. Receiver operator curves were generated.

    RESULTS:

    The frequency of gene methylation ranged from 6.3% (p14) to 83.2% (GSTP1) in prostate cancer patients. At least one gene was hypermethylated in 93% of cancer patients. The specificity of methylation was 0.74. Methylation was significantly more frequent (P < 0.05) in cancer than control patients for all genes except p14 and p16. According to receiver operator curve analysis, the four-gene combination of GSTP1 (0.86), RASSF1a (0.85), RARbeta2 (0.80), and APC (0.74) best discriminated malignant from nonmalignant cases. The sensitivity and accuracy of this four-gene set were 86% and 89%, respectively.

    CONCLUSIONS:

    The presence of aberrant methylation in urinary cells obtained after prostate massage is significantly associated with prostate cancer. A panel of four genes could stratify patients into low and high risk of having prostate cancer and optimize the need for repeat prostatic biopsies.

    PMID:
    17363525
    [PubMed - indexed for MEDLINE]
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