Clinical Research

Quadriplex model enhances urine-based detection of prostate cancer



The major advantages of urine-based assays are their non-invasive character and ability to monitor prostate cancer (CaP) with heterogeneous foci. While the test for the prostate cancer antigen 3 (PCA3) is commercially available, the aim of our research was to test other putative urine markers in multiplex settings (AMACR (α-methylacyl-CoA racemase), EZH2 (enhancer of zeste homolog 2), GOLM1 (golgi membrane protein 1), MSMB (microseminoprotein, β), SPINK1 (serine peptidase inhibitor) and TRPM8 (transient receptor potential cation channel, subfamily M, member 8)).


Expression of the candidate biomarkers was studied in sedimented urine using quantitative reverse transcriptase polymerase chain reaction in two sets of patients with and without restriction on serum PSA levels.


We confirmed that PCA3 is an independent predictor of cancer in the patients without restriction of serum PSA values (set 1, n=176, PSA=0.1–587 ng ml–1). However, AMACR was the only parameter that differentiated CaP from non-CaP patients with serum PSA between 3 and 15 ng ml–1 (set 2, n=104). The area under curve (AUC) for this gene was 0.645 with both sensitivity and specificity at 65%. Further improvement was achieved by multivariate logistic regression analysis, which identified novel duplex (TRPM8 and MSMB), triplex (plus AMACR) and quadriplex (plus PCA3) models for the detection of early CaPs (AUC=0.665, 0.726 and 0.741, respectively).


Novel quadriplex test could be implemented as an adjunct to serum PSA or urine PCA3 and this could improve decision making for diagnostics in the case of ‘PSA dilemma’ patients.


Current research devotes considerable effort in the search of non-invasively detectable biomarkers that can supplement the widely used serum PSA and thus reduce the number of unnecessary biopsies in ‘PSA dilemma’ patients (4–10 ng ml–1). Transrectal prostate biopsy is invasive and neoplastic tissue can be easily missed as scant samples cannot completely reflect the polyclonal nature of prostate cancer (CaP).1 Like prostate biopsies, a single biomarker test is also incapable of reflecting the heterogenity of cancer development.2, 3 Urine is readily available. It contains exfoliated cells and secreted products released from multiple foci, which can be collected and used in early CaP detection. Multiplex urine-based assays have advantages of being non-invasive, capturing heterogeneous tumor foci and subsequently detecting cancer more accurately than do single marker tests.2, 3 They might supplement serum PSA testing and reduce the number of unnecessary biopsies, thereby obviating the complications associated with biopsy.2, 3, 4 The multiplex model of cancer detection was first introduced by Laxman et al.5 who proposed combined evaluation of the multiple CaP-associated markers in urine in order to significantly increase detection rates over serum PSA or PCA3 (prostate cancer antigen 3) alone. Several studies prompted us to evaluate multiple biomarkers, which could serve as an adjunct for urinary PCA3 in order to increase its specificity and sensitivity.3, 5, 6, 7 Markers evaluated in our study along with PCA3 were AMACR (α-methylacyl-CoA racemase), SPINK1 (serine peptidase inhibitor, Kazal type 1), EZH2 (enhancer of zeste homolog 2), GOLM1 (golgi membrane protein 1), TRPM8 (transient receptor potential cation channel, subfamily M, member 8) and MSMB (microseminoprotein, β).

Materials and methods

Patients, urine collection, RNA isolation and preamplification

Urine samples were obtained from 314 randomly chosen patients who were scheduled for needle biopsy, radical prostatectomy or other examination owing to urological complaints. The final number of patients included in the statistical analysis was 176 due to exclusion of samples with low quality or concentration of RNA. The cohort consisted of both positive needle biopsy and radical prostatectomy patients (CaP, n=91) and non-cancer patients (non-CaP, n=85) with BPH or prostatitis. Clinicopathological characteristics were defined according to the WHO classification and are listed in Table 1. After signing an informed consent statement approved by the Ethics Committee of the Medical Faculty of Palacky University, patients were asked to provide 20–50 ml urine after attentive digital rectal examination. Total RNA isolation was performed by the Urine Exfoliated Cell RNA Purification Kit (Fisher Scientific, Waltham, MA, USA), quantified by Nanodrop, pretreated with Dnase I (Invitrogen, Carlsbad, CA, USA) and reverse transcribed with SuperScript III Reverse Transcriptase (Invitrogen). The uniformity test as well as cDNA preamplification was performed according to Noutsias et al.8 and the TaqMan PreAmp Master Mix Kit (Applied Biosystems, Foster City, CA, USA), which is intended for samples with limited amount of RNA. The uniformity test was performed before preamplification in order to check whether all amplicons were amplified uniformly without bias. Briefly, relative quantification was done and ΔΔCt values were calculated for both non-amplified and preamplified cDNA.9 Uniformly amplified targets should produce ΔΔCt values within ±1.5. A measure of 5 μl of cDNA from each sample was amplified separately with two sets of primer pairs (synthesized by Generi-Biotech, Hradec Králové, Czech Republic) in order to avoid primer–dimer formation and unspecific amplification (mix.1: PCA3, PSA, AMACR, SPINK1, EZH2 and mix.2: GOLM1, TRPM8, MSMB; Table 2 and Supplementary Information). After 14 cycles at 94–60–72 °C, preamplified cDNA products were diluted in water (1:10) and stored at −20 °C.

Table 1 Patient characteristics
Table 2 Primers and probes used in qPCR

Quantitative polymerase chain reaction analysis

Quantitative polymerase chain reaction was used to validate the diagnostic ability of PCA3 in the randomly selected patients with serum PSA values of 0.1–587 ng ml–1 (n=176). Urine PSA was measured to confirm enough prostate epithelial material and normalize PCA3Ct=Ct PCA3Ct PSA). Samples that had PSA Ct values >30 were excluded and deemed ‘non-evaluable’ (please see Supplementary Information for further details) though we did not exclude samples with negative Ct PSA values but with strong positive PCA3 values (low Ct values). We then selected patients with PSA=3–15 ng ml–1 (n=104) and multiplex evaluation was done on seven CaP-related biomarkers (AMACR, SPINK1, EZH2, GOLM1, TRPM8, MSMB) in order to complement PCA3 and improve diagnostic accuracy for ‘PSA dilemma’ patients (Table 1). Primers and probes were designed by ProbeFinder software (Roche, Basel, Switzerland, Universal Probe Library assay design center) (Table 2). The real-time polymerase chain reaction reactions were performed with LightCycler 480 (Roche) Probes Master Mix for 50 cycles of denaturation, annealing and extension (95–60–72 °C each for 20 s). Relative quantification was carried out according to the ΔCt method using a reference gene (ΔCt=Ct targetCt PSA)9 and inverse values of ΔCt (−ΔCt) were used for subsequent statistical analysis and visualization.5

Statistical analysis

The data were analyzed using the Statistical Package for Social Sciences (SPSS, Chicago, IL, USA). The Mann–Whitney test was used for comparisons of the relative expression of biomarkers in CaP and non-CaP patients, clinical and pathological stages, low- and high-grade cancers and different risk group patients. Correlations between gene expressions were made by the Spearman's rank correlation coefficient.

Univariate and multivariate logistic regressions were used to examine associations between CaP diagnostic status and test variables and determine whether the marker or marker combination could predict the presence of the cancer. Akaike information criterion-based backward stepwise selection strategy was used in order to exclude insignificant markers, that is several combinations of given markers were analyzed, subtracting each marker at each step until the optimal combination was found. Leave-one-out cross-validation strategy was used in all steps to avoid overoptimization of area under curves (AUCs) in the regression analysis.5 The validity and quality of the resulting logit models were assessed by Hosmer and Lemeshow goodness-of-fit tests, by P-values of each regression parameter, and by the estimated AUC.

Receiver-operating characteristic (ROC) curves were calculated in order to assess the diagnostic power of variables tested univariately or by the multivariate analysis using the AUC. Optimal sensitivity, specificity, accuracy and positive and negative predictive values were calculated for selected markers and their combinations. Statistical significance was considered when two-sided P<0.05.


First, we evaluated PCA3 marker alone in the patients without restriction of serum PSA values (full range PSA=0.1–587 ng ml–1; n=176). Univariate logistic regression analysis showed that expression of PCA3 was an independent predictor of cancer (β coefficient=0.068, P<0.001) and a significant discriminator of CaP from non-malignant cases (P<0.001). The diagnostic performance of the PCA3 was evaluated using ROC analysis based on the predicted probabilities of the logistic model (Figure 1a; AUC=0.671, 95% confidence interval: 0.592–0.751, P<0.001). After performing cross-validation, differences between initial AUCs were estimated to be small and negligible. We found no differences between clinicopathological parameters and PCA3 expression status. As expected, PSA had significantly higher levels in advanced stages (localized CaP<locally advanced (P<0.001)<metastatic CaP (P=0.005)).

Figure 1

Prostate cancer antigen 3 (PCA3) in the full range of serum PSA (0.1–587 ng ml–1). (a) Area under curve for PCA3 was 0.671 (P<0.001, receiver-operating characteristic analysis). (b) Expression difference of PCA3 between prostate cancer (CaP) and non-CaP patients (P<0.001, Mann–Whitney analysis). Box-and-whisker plot displays median, 25–75% percentiles and 95% confidence interval without outliers. Commonly used PCA3 score cutoff 35 (for example 35 copies of PCA3 mRNA)/(for example 1000 copies of PSA mRNA) × 1000 matches to dCt 4.8 (with optimal polymerase chain reaction efficiency), which well corresponds with our median dCt values (4.6 and 3.5 for non-cancer and CaP patients, respectively).

As the primary aim of urine assays was to assist in identification of patients for diagnostic biopsies, we focused in the following analysis on patients with PSA range 3–15 ng ml–1 (n=104). Seven biomarkers (PCA3, AMACR, SPINK1, EZH2, GOLM1, TRPM8 and MSMB) were analyzed in parallel. However, only AMACR statistically differentiated CaP patients from patients with non-malignant disease (Figure 2e; Table 3). The diagnostic performance of AMACR was also evaluated by ROC analysis based on the predicted probabilities derived from univariate regression analysis (AUC=0.626, P=0.040). To further evaluate if the multiplex model could improve performance over single biomarkers, we used a multivariate logistic regression analysis plus Akaike information criterion-based backward stepwise selection strategy, which is based on excluding insignificant markers. Four markers, PCA3, AMACR, MSMB and TRPM8, yielded the highest Wald values (1.643, 3.145, 6.191 and 7.421, respectively), which reflect the contribution of each variable in the multivariate regression analysis. Both quadriplex and triplex models retained the same characteristics as the model with all seven markers and thus can be implemented in routine diagnostic procedures (Table 3; Figure 2).

Figure 2

Receiver-operating characteristic analysis for the early diagnosis of prostate cancer in the serum PSA zone of 3–15 ng ml–1. (a) Multiplex model was composed of all biomarkers and serum PSA (N=87). (b) Quadriplex model included prostate cancer antigen 3 (PCA3); α-methylacyl-CoA racemase (AMACR); transient receptor potential cation channel, subfamily M, member 8 (TRPM8) and microseminoprotein, β (MSMB) (N=87). (c) Triplex model included AMACR, TRPM8 and MSMB (N=87). (d) Duplex model included TRPM8 and MSMB (N=87). (e) Univariately tested AMACR (N=94). (f) Univariately tested PCA3 (N=104). AUC, area under curve; NPV, negative predictive value; PPV, positive predictive value; Sens., sensitivity; Spec., specificity.

Table 3 Logistic regression and ROC analyses of the biomarkers in the range of PSA 3–15 ng ml–1

Finally, we tested correlations between markers and any associations with clinicopathological characteristics. Several significant correlations were found with the strongest positive correlation between TRPM8 and MSMB (r=0.675 in the whole patient set, r=0.787 in CaP and r=0.492 in non-CaP patients; all P<0.01) (Table 4). Next, we found higher expression of AMACR and EZH2 in locally advanced cancer vs localized cancer (P=0.044 and 0.022, respectively) (Figure 3a). EZH2 expression was also gradually increased along with cancer progression (low risk<intermediate (P=0.044) <high risk (P=0.067) (Figure 3b). An inverse association was found for PCA3 in the low- and high-risk group of patients (P=0.047) (Figure 3b). No differences in marker expressions were observed between low- and high-grade tumors (Gleason score <7 vs 7). As expected, higher levels of serum PSA (P=0.036) and also EZH2 (P=0.015) and GOLM1 (P=0.048) were found in patients with abnormal digital rectal examination and high probability of cancer (P=0.001).

Table 4 Non-parametric Spearman's rank correlation analysis between biomarkers
Figure 3

Expression differences between localized and locally advanced cancers and risk groups in the range of serum PSA=3–15 ng ml–1. (a) α-Methylacyl-CoA racemase (AMACR) and enhancer of zeste homolog 2 (EZH2) were significantly overexpressed in locally advanced cancers compared with localized ones (P=0.044 and 0.022, respectively). (b) Significant differences were found for EZH2 expression in high- vs low-risk group patients (P=0.002), intermediate- vs low-risk group patients (P=0.044) and a trend of higher expression between high- and intermediate-risk group patients (P=0.067). Prostate cancer antigen 3 (PCA3) was inversely expressed in risk group patients, that is overexpressed in low- vs high-risk group patients (P=0.047). Box-and-whisker plots display median, 25–75% percentiles and 95% confidence interval without outliers. CaP, prostate cancer.


We confirmed that PCA3 can successfully discriminate CaP from non-CaP in randomly chosen patients with variable PSA levels (PSA=0.1–587 ng ml–1).10, 11, 12, 13 However, we could not confirm the diagnostic ability of PCA3 in patients with serum PSA of 3–15 ng ml–1. This might be explained by the technologies used in commercial assays such as target capture with magnetic particles, transcription-mediated amplification and hybridization protection assay to detect PCA3 transcripts in urine.11, 12, 13 For this reason, direct comparison of our results with these studies is inappropriate. The main grounds for not using the commercial CaP urinary test system were the high costs of the tests and the fact that this assay uses a closed system, which does not allow the user to test in parallel additional biomarkers.5, 14

The second part of the analysis was hence done on a multiplex model to complement PCA3 and improve diagnostic accuracy in the case of ‘PSA dilemma’ patients. Markers were chosen according to previous studies as being either detectable in urine or appropriate for combined models. AMACR,7, 15, 16 SPINK1,5, 17 EZH2,3, 6 GOLM1,5, 18 TRPM8 are overexpressed in CaP,3, 6, 19, 20 while MSMB is prostate specific but underexpressed in cancer.21, 22, 23 Only AMACR was a significant predictor of cancer in the univariate analysis. The ROC curve and Mann–Whitney analysis confirmed that AMACR could complement PCA3 testing as it was shown by Ouyang et al.7 who achieved significantly higher sensitivity and specificity when both markers were combined. Consistent with other studies we also confirmed the idea of using AMACR as a marker of progression as its expression was increased in advanced stages of the cancer.24, 25

The multiplex model, which was composed of all seven biomarkers including serum PSA, showed significantly good AUC (AUC=0.744). Further, as this model would not be cost- and labor-effective, three additional models were generated. The quadriplex model retained most of the good characteristics of the multiplex model (AUC=0.741). Thus, we were able to prove the usefulness of evaluating AMACR, TRPM8 and MSMB in combined settings that could serve as an adjunct to PCA3.3, 6, 7, 16, 23 It should be noted that two patients (only one in the multiplex study; this man had increased expression in particular of PCA3 and AMACR) have recently been reclassified as cancer positive (after repeated biopsy) and therefore statistical re-analysis might further improve characteristics of the multiplex model. We also consider additional urine markers for our future patients, in particular a novel panel of TMPRSS2–ERG fusion transcripts.26

We found no association between individual marker expression and clinicopathological parameters with the exception of AMACR (see above) and EZH2. Expression of EZH2 was increased in advanced cancers, consistent with other studies that have suggested this as a marker of prognosis.27, 28 As expected, serum PSA levels were also significantly increased along with cancer progression. Surprisingly, PCA3 had a slightly decreased expression in the high-risk group of patients in contrast to EZH2, which supports the idea of being PCA3 as a marker of early diagnosis of CaP.11, 12, 13 Interestingly, we also found a strong positive correlation between TRPM8 and MSMB but future studies are needed to validate and clarify this association.

In summary, we demonstrated that novel multiplex quantitative polymerase chain reaction assay on sedimented urine from ‘PSA dilemma’ patients could be implemented as an adjunct to the routine diagnostic analysis of CaP such as serum PSA or PCA3 and may be used to improve decision making for repeat biopsies in men with elevated PSA levels. Noteworthy is that the quadriplex urine test presented here achieves a higher specificity than PCA3 for ‘PSA dilemma’ patients, which could reduce negative biopsies and rule out clinically significant CaP. Future studies will be directed to further improve the performance of this test by examination of larger cohorts on novel combined models.


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This work was supported by Grants NS 9940-4 from the Czech Ministry of Health and MSM 6198959216 from the Czech Ministry of Education and EU infrastructure support CZ.1.05/2.1.00/01.0030. Tamar Jamaspishvili was also supported by GACR 303/09/H048 from the Grant Agency of the Czech Republic and LF_2010_006. We sincerely thank Jana Holinkova for her excellent technical assistance.

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Correspondence to J Bouchal.

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Supplementary Information accompanies the paper on the Prostate Cancer and Prostatic Diseases website

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Jamaspishvili, T., Kral, M., Khomeriki, I. et al. Quadriplex model enhances urine-based detection of prostate cancer. Prostate Cancer Prostatic Dis 14, 354–360 (2011).

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  • quadriplex model
  • multiplex model
  • early diagnosis of prostate cancer
  • urine

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