This study aims to evaluate the effectiveness and clinical performance of a panel of urinary biomarkers to diagnose prostate cancer (PCa) in Chinese men with PSA levels between 4 and 10 ng/mL. A total of 122 patients with PSA levels between 4 and 10 ng/mL who underwent consecutive prostate biopsy at three hospitals in China were recruited. First-catch urine samples were collected after an attentive prostate massage. Urinary mRNA levels were measured by quantitative real-time polymerase chain reaction (qRT-PCR). The predictive accuracy of these biomarkers and prediction models was assessed by the area under the curve (AUC) of the receiver-operating characteristic (ROC) curve. The diagnostic accuracy of PCA3, PSGR, and MALAT-1 was superior to that of PSA. PCA3 performed best, with an AUC of 0.734 (95% CI: 0.641, 0.828) followed by MALAT-1 with an AUC of 0.727 (95% CI: 0.625, 0.829) and PSGR with an AUC of 0.666 (95% CI: 0.575, 0.749). The diagnostic panel with age, prostate volume, % fPSA, PCA3 score, PSGR score, and MALAT-1 score yielded an AUC of 0.857 (95% CI: 0.780, 0.933). At a threshold probability of 20%, 47.2% of unnecessary biopsies may be avoided whereas only 6.2% of PCa cases may be missed. This urinary panel may improve the current diagnostic modality in Chinese men with PSA levels between 4 and 10 ng/mL.
The diagnosis of prostate cancer (PCa) has mostly relied on prostate-specific antigen (PSA) levels and digital rectal examinations (DRE) in the past decades [
Nevertheless, the diagnostic accuracy in current clinical practice is still far from satisfactory even with these novel biomarkers, perhaps due to the heterogeneity of PCa itself [
This study was approved by the Institutional Review Board of The People’s Hospital of Wujiang City, Shanghai Shibei Hospital, and The Third People’s Hospital of Yancheng. The methods were carried out in accordance with approved guidelines. The informed consent of all patients was obtained.
The study was launched and led by The People’s Hospital of Wujiang City. All sites shared the same standard operating procedure (SOP) for patient inclusion and sample processing. All the patients were evaluated and recruited in the outpatient department of each site. The inclusion criteria of patients were as follows: (1) men aged 45 years or older with or without family history of prostate cancer; (2) a PSA level between 4 and 10 ng/mL; (3) with or without an abnormal digital rectal examination (DRE); and (4) scheduled for transrectal ultrasound (TRUS) guided systematic prostate biopsy as part of routine medical care. All sites performed a standardized 10–12 core biopsy protocol.
The indications for biopsy were elevated PSA level (4–10 ng/mL) and/or suspicious findings in digital rectal examination (DRE). Patients who had suspicions of urinary tract infections or who received catheterization of the urethra within the previous 2 weeks were excluded. Patients with other known tumors, medical therapy known to affect serum PSA levels, and/or previous treatment for PCa were excluded. Urine samples were collected before prostate biopsy. The prospectively enrolled patients underwent prostate biopsy at The People’s Hospital of Wujiang City (70 cases), Shanghai Shibei Hospital (60 cases), and The Third People’s Hospital of Yancheng (30 cases).
Biopsies were performed using an end-fire ultrasound transducer (Falcon 2101; B-K Medical, Inc.) and an automatic 18-gauge needle (Bard, Inc.). In all men, a 10–12-core systematic, laterally directed, TRUS-guided biopsy was performed.
First-catch urine samples were collected following an attentive prostate massage (three strokes for each lobe) before biopsy. The urine samples were cooled immediately on ice and were further processed within 2 hours from collection. Samples were further centrifuged at 2,500 ×g for 15 min at 4°C. The pellets were washed twice with cold PBS (1x). The sediments were then homogenized in TRIzol reagent (Invitrogen: number 15596-026, USA) for RNA extraction and stored at −80°C for further use. The samples were shipped on dry ice and tested at the central laboratory of The People’s Hospital of Wujiang City.
In total, 50 ng of RNA was treated with DNase I (TaKaRa: D2215, TaKaRa, Japan) prior to cDNA synthesis and then amplified using the TransPlex Complete Whole Transcriptome Amplification Kit (WTA2, Sigma-Aldrich, St. Louis, MO, USA). Furthermore, SYBR® Premix Ex Taq™ (Perfect Real Time) (Takara: DRR081A TaKaRa, Japan) was applied in qRT-PCR tests using Applied Biosystems Step One Plus. Cycling conditions were in accordance with the manufacturer’s recommendations. The qRT-PCR primers were as follows: PSA-forward primer GTCTGCGGCGGTGTTCTG, PSA-reverse primer TGCCGACCCAGCAAGATC; PCA3 forward primer TGGTGGGAAGGACCTGATGATACAG, PCA3 reverse primer TCTCCCAGGGATCTCTGTGCTTCC; PSMA forward primer GCCCACAGGAACAAGTCCTA, reverse primer CTCTGCAATTCCACGCCTAT; PSGR forward primer CATGGCCTTTGACCGTTATGT, reverse primer GCCAATCTGGGCTGTTACTGTAT; and MALAT1 forward primer CTTCCCTAGGGGATTTCAGG, MALAT1 reverse primer GCCCACAGGAACAAGTCCTA. Briefly, 2
Baseline information of the patients and their biomarker scores for positive and negative biopsies were compared using the Mann–Whitney
Initially, 160 patients were included in this study. Among them, 14 samples were excluded for insufficient RNA extraction. After quantitative RT-PCR analysis, another 24 patients were excluded for PSA Ct values over 28 [
Clinical characteristic of men with positive and negative biopsy.
Median (IQR) | All patients | | |
---|---|---|---|
Prostate cancer | Negative biopsy | ||
No. pts | 33 | 89 | |
Age, mean (SD), years | 68.4 (6.7) | 64.1 (7.7) | |
PSA, mean (SD), ng/mL | 7.0 (1.7) | 7.2 (1.7) | |
Prostate volume, mL | 51.6 (38.2, 69.7) | 39.9 (28.0, 55.9) | |
% fPSA, % | 15.0 (10.6, 18.5) | 17.0 (13.3 to 22.7) | |
PCA3 | 125.0 (48.4, 252.7) | 51.5 (14.5 to 104.7) | |
PSGR | 160.3 (111.5, 298.9) | 105.8 (32.9 to 187.0) | |
PSMA | 62.7 (22.5, 120.9) | 69.2 (25.4, 116.0) | |
MALTA-1 | 160.3 (101.4 to 499.5) | 85.5 (31.1 to 151.8) | |
Positive DRE | 11/33 | 20/89 | |
IQR: interquartile range; no. pts: number of patients; PSA: prostate-specific antigen; % fPSA: percent free PSA; PCA3: prostate cancer antigen 3; PSGR: prostate-specific G protein coupled receptor; PSMA: prostate-specific membrane antigen; MALAT-1: metastasis-associated lung adenocarcinoma transcript 1;
Comparison of PCA3 score (a), PSGR score (b), PSMA score (c), and MALAT-1 score (d) of positive and negative biopsies.
Age, prostate volume, % fPSA, DRE results, PCA3 score, PSGR score, and MALAT-1 score were significant predictors for biopsy results in the univariate logistic regression analysis whereas PSA and PSMA were not (Table
Univariate logistic regression analyses of predictors for predicting prostate cancer.
Variables | OR (95% CI); | AUC (95% CI) |
---|---|---|
Age | 1.089 (1.0227, 1.160); 0.004 | 0.668 (0.577, 0.750) |
PSA | 0.945 (0.747, 1.197); 0.640 | 0.525 (0.408, 0.642) |
Prostate volume | 0.975 (0.954, 0.996); 0.020 | 0.657 (0.566, 0.741) |
% fPSA | 0.0003 (0, 0.241); 0.018 | 0.617 (0.524, 0.703) |
DRE | 1.725 (0.717, 4.152); 0.224 | 0.554 (0.437, 0.672) |
PCA3 | 1.006 (1.002, 1.010); 0.001 | 0.734 (0.641, 0.828) |
PGSR | 1.002 (1.000, 1.004); 0.026 | 0.666 (0.575, 0.749) |
PSMA | 0.999 (0.995, 1.003); 0.621 | 0.516 (0.398, 0.634) |
MALAT-1 | 1.003 (1.001, 1.005); 0.002 | 0.727 (0.625, 0.829) |
PSA: prostate-specific antigen; % fPSA: percent free PSA; DRE: positive digital rectal examination results; PCA3: prostate cancer antigen 3; PSGR: prostate-specific G protein coupled receptor; PSMA: prostate-specific membrane antigen; MALAT-1: metastasis-associated lung adenocarcinoma transcript 1.
The diagnostic performance of PCa3, PSGR, and MALAT-1 is characterized in Table
Sensitivity and specificity of three biomarkers with efficacy in predicting prostate cancer in patients with PSA 4–10 ng/mL.
— | Criterion | Sensitivity | 95% CI | Specificity | 95% CI | +LR | −LR | +PV | −PV |
---|---|---|---|---|---|---|---|---|---|
PCA3 | 23.5 | 97.0 | 84.2–99.9 | 41.6 | 31.2–52.5 | 1.7 | 0.1 | 38.1 | 97.4 |
30.4 | 90.9 | 75.7–98.1 | 43.8 | 33.3–54.7 | 1.6 | 0.2 | 37.5 | 92.9 | |
35.4 | 81.8 | 64.5–93.0 | 46.1 | 35.4–57.0 | 1.5 | 0.4 | 36.0 | 87.2 | |
214.3 | 27.3 | 13.3–45.5 | 89.9 | 81.7–95.3 | 2.7 | 0.8 | 50.0 | 76.9 | |
| |||||||||
PSGR | 24.2 | 90.9 | 75.7–98.1 | 20.2 | 12.4–30.1 | 1.1 | 0.5 | 29.7 | 85.7 |
93.0 | 81.8 | 64.5–93.0 | 49.4 | 38.7–60.2 | 1.6 | 0.4 | 37.5 | 88.0 | |
623.3 | 12.1 | 3.4–28.2 | 96.6 | 90.5–99.3 | 3.6 | 0.9 | 57.1 | 74.8 | |
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MALAT-1 | 32.8 | 90.9 | 75.7–98.1 | 25.8 | 17.1–36.2 | 1.2 | 0.4 | 31.3 | 88.5 |
109.6 | 72.7 | 54.5–86.7 | 60.7 | 49.7–70.9 | 1.9 | 0.5 | 40.7 | 85.7 | |
156.5 | 54.6 | 36.4–71.9 | 78.7 | 68.7–86.6 | 2.6 | 0.6 | 48.6 | 82.4 | |
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PSA | 9.6 | 90.9 | 75.7–98.1 | 11.2 | 5.5–19.7 | 1.02 | 0.81 | 27.5 | 76.9 |
7.95 | 72.7 | 54.5–86.7 | 39.3 | 29.1–50.3 | 1.20 | 0.69 | 30.8 | 79.5 | |
5.1 | 18.18 | 7.0–35.5 | 91.0 | 83.1–96.0 | 2.02 | 0.90 | 42.9 | 75.0 | |
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% fPSA | 21.9 | 90.9 | 75.7–98.1 | 28.1 | 19.1–38.6 | 1.26 | 0.32 | 31.9 | 89.3 |
23.2 | 100.0 | 89.4-100.0 | 24.7 | 16.2–35.0 | 1.33 | 0.00 | 33.0 | 100.0 | |
9.1 | 21.2 | 9.0–38.9 | 89.9 | 81.7–95.3 | 2.10 | 0.88 | 43.7 | 75.5 |
Only three predictors were included in the base prediction model (age, prostate volume, and % fPSA) in multivariate logistic regression analysis with a predicted accuracy of 74.6% and an AUC of 0.733 (95% CI: 0.634, 0.831). However, when we added the three novel biomarkers (PCA3, PSGR, and MALAT-1) to the base model to establish an improved model, we found that the predictive accuracy was improved to 84.4% and the AUC of the improved model was 0.846 (95% CI: 0.766, 0.927) (Table
Multivariate logistic regression analyses of the base model and the improved model for predicting prostate cancer.
Variables | Base model‡ | Improved model |
---|---|---|
OR (95% CI); | OR (95% CI); | |
Age | 1.080 (1.010, 1.155); 0.024 | 1.058 (0.978, 1.144); 0.159 |
Prostate volume | 0.979 (0.958, 1.001); 0.063 | 0.984 (0.960, 1.009); 0.221 |
% fPSA | 0.001 (0.00001, 1.438): 0.063 | 0.00001 (0, 0.277); 0.024 |
PCA3 | — | 1.008 (1.003, 1.012); 0.002 |
PGSR | — | 1.002 (1.000, 1.005); 0.036 |
MALAT-1 | — | 1.004 (1.001, 1.006); 0.003 |
PA (%) | 74.6% | 84.4% |
Increment PA (%) | — | 9.8% |
AUC (95% CI) | 0.733 (0.634, 0.831) | 0.857 (0.780, 0.933) |
Increment AUC (95% CI) | — | 0.124 |
PCA3: prostate cancer antigen 3; PSGR: prostate-specific G protein coupled receptor; MALAT-1: metastasis-associated lung adenocarcinoma transcript 1; AUC: area under the curve; 95% CI: 95% confidential interval.
Receiver-operating characteristic curve analysis for evaluating the diagnostic performance of PCA3 score (a), PSGR score (b), PSMA score (c), MALAT-1 score (d), their comparison (e), and the base and improved models (f).
The results of the decision curve analysis indicated that the improved model was superior to the base model in the defined range of clinical interest (10–40%) with a higher net benefit (Figure
Net benefit and reduction in avoidable biopsies in predicting high-grade prostate cancer for the base model and improved model compared to the ‘‘treat-all’’ strategy to biopsy every patient for different threshold probabilities in the same range.
Threshold probability (%) | 15 | 20 | 25 | 30 | 35 | 40 | |
---|---|---|---|---|---|---|---|
Net benefit | Base model | 17.3 | 11.1 | 9.0 | 7.5 | 7.1 | 4.9 |
Improved model | 19.2 | 17.4 | 14.8 | 12.2 | 10.2 | 10.9 | |
Treat all | 14.2 | 8.8 | 2.7 | −4.2 | −12.2 | −21.6 | |
| |||||||
Net reduction in the number of biopsies | Base model | 17.8 | 9.0 | 18.9 | 27.3 | 35.9 | 39.8 |
Improved model | 28.7 | 34.4 | 36.1 | 38.3 | 41.6 | 48.8 |
Number of high-grade prostate cancers missed and reduction in biopsies according to threshold probability in the range of 10–40% for the base model and improved model.
Probability cutoff, % | Model | PCa missed, number (%) | High-grade PCa missed, number (%) | Unnecessary biopsies spared, number (%) |
---|---|---|---|---|
15 | Base model | 1 (3.0%) | 0 (0%) | 22 (24.4%) |
Improved model | 1 (3.0%) | 1 (3.0%) | 35 (39.3%) | |
20 | Base model | 1 (3.0%) | 0 (0%) | 11 (12.3%) |
Improved model | 2 (6.1%) | 1 (3.0%) | 42 (47.2%) | |
25 | Base model | 1 (3.0%) | 0 (0%) | 23 (25.9%) |
Improved model | 3 (9.1%) | 2 (6.1%) | 44 (49.5%) | |
30 | Base model | 2 (6.1%) | 1 (3.0%) | 33 (37.4%) |
Improved model | 3 (9.1%) | 2 (6.1%) | 47 (52.5%) | |
35 | Base model | 4 (12.1%) | 3 (9.1%) | 44 (49.2%) |
Improved model | 3 (9.1%) | 2 (6.1%) | 51 (57.0%) | |
40 | Base model | 7 (21.2%) | 5 (15.2%) | 49 (54.6%) |
Improved model | 5 (15.2%) | 4 (12.1%) | 60 (66.9%) |
Decision curve analysis for positive biopsy prediction by the base and improved models. The dashed black line indicates the base model; the dashed red line shows the improved model. The horizontal line along the
To the best of our knowledge, this is the first study investigating the diagnostic performance of PCA3, PSGR, MALAT-1, and PSMA in an Asian population. We have validated that PCA3, PSGR, and MALAT-1 scores were able to discriminate PCa patients from patients with negative biopsies. Further analyses indicated that the prediction model incorporating these three biomarkers improves the diagnostic accuracy compared with the current clinical modality. The decision curve analysis illustrated that this prediction model would greatly benefit patients undergoing prostate biopsy by reducing the number of unnecessary biopsies.
There is some strength in this study. First, this study presented the first evaluation of these four biomarkers in predicting PCa in Chinese men with PSA levels between 4 and 10 ng/mL. The AUC of the PCA3 score is the highest, followed by MALTA-1 and PSGR. Second, although the diagnostic performance of biomarker panels similar to those in this study was previously validated in Western populations, this study is the first that was conducted in Asians that demonstrated an improved diagnostic performance (AUC = 0.846). Third, this study tested whether novel biomarker panels would significantly improve the diagnosis of PCa; we found that MALAT-1 has similar discriminative power to that of PCA3, which validated our previous study [
The diagnosis of PCa in patients with PSA levels between 4 and 10 ng/mL is quite challenging because patients with PSA levels over 10 ng/mL have a much higher risk of PCa; however, it is rather difficult to differentiate cases with PCa from those without PCa in men with PSA levels between 4 and 10 ng/mL, the so-called “PSA grey zone.” The combined performance of this urinary biomarker panel is relatively high in patients with PSA levels between 4 and 10 ng/mL, especially in clinical practice when considering the threshold probability of triggering a prostate biopsy. As reported above, if a doctor biopsied a man only if his probability of PCa was over 20%, this improved model would save almost half (47.2%) of patients from unnecessary biopsies, at the cost of missing only 2 PCa patients (including 1 high-grade PCa case). In addition, these urinary biomarkers could be measured simultaneously with only one urine sample, which makes this method highly cost-effective. We consider this improvement of clinical relevance and such evidence is supported by the application of this panel in Chinese men.
Inconsistent with previous studies in Western populations, PSMA was not a significant predictor of the biopsy results in this study. PSMA protein was shown to be upregulated in PCa tissue compared with benign prostate tissues [
In conclusion, we demonstrated that urinary RT-PCR based PCA3, PSGR, and MALAT-1 scores and panels of these biomarkers in combination could serve as a noninvasive method for detecting PCa in Chinese men with PSA levels between 4 and 10 ng/mL. Applying a probability threshold of 20%, the improved model would avoid almost half of unnecessary biopsies while only missing 6.2% of PCa cases. Future large-scale studies are needed to confirm the efficacy of this panel in the diagnosis of prostate cancer.
The authors declare that there are no competing financial interests.
Yongqiang Zhou, Yun Li, and Minjun Jiang designed the experiments. Yongqiang Zhou, Yun Li, and Xiangnan Li collected and prepared the clinical samples. Yongqiang Zhou and Yun Li performed the qRT-PCR. Yongqiang Zhou and Xiangnan Li analyzed the data. All authors drafted and revised the manuscript. All authors read and approved the final version of the manuscript. Yongqiang Zhou, Yun Li, and Xiangnan Li contributed equally to this article.