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International Brazilian Journal of Urology : Official Journal of the Brazilian Society of Urology logoLink to International Brazilian Journal of Urology : Official Journal of the Brazilian Society of Urology
. 2020 Jul 31;46(5):691–704. doi: 10.1590/S1677-5538.IBJU.2019.0360

Diagnosis accuracy of PCA3 level in patients with prostate cancer: a systematic review with meta-analysis

Zhiqiang Qin 1, Jianxiang Yao 2, Luwei Xu 1, Zheng Xu 1, Yuzheng Ge 1, Liuhua Zhou 1, Feng Zhao 1, Ruipeng Jia 1,
PMCID: PMC7822358  PMID: 31961625

ABSTRACT

Background:

The diagnostic value and suitability of prostate cancer antigen 3 (PCA3) for the detection of prostate cancer (PCa) have been inconsistent in previous studies. Thus, the aim of the present meta-analysis was performed to systematically evaluate the diagnostic value of PCA3 for PCa.

Materials and Methods:

A meta-analysis was performed to search relevant studies using online databases EMBASE, PubMed and Web of Science published until February 1st, 2019. Ultimately, 65 studies met the inclusion criteria for this meta-analysis with 8.139 cases and 14.116 controls. The sensitivity, specificity, positive likelihood ratios (LR+), negative likelihood ratios (LR−), and other measures of PCA3 were pooled and determined to evaluate the diagnostic rate of PCa by the random-effect model.

Results:

With PCA3, the pooled overall diagnostic sensitivity, specificity, LR+, LR−, and 95% confidence intervals (CIs) for predicting significant PCa were 0.68 (0.64-0.72), 0.72 (0.68-0.75), 2.41 (2.16-2.69), 0.44 (0.40-0.49), respectively. Besides, the summary diagnostic odds ratio (DOR) and 95% CIs for PCA3 was 5.44 (4.53-6.53). In addition, the area under summary receiver operating characteristic (sROC) curves and 95% CIs was 0.76 (0.72-0.79). The major design deficiencies of included studies were differential verification bias, and a lack of clear inclusion and exclusion criteria.

Conclusions:

The results of this meta-analysis suggested that PCA3 was a non-invasive method with the acceptable sensitivity and specificity in the diagnosis of PCa, to distinguish between patients and healthy individuals. To validate the potential applicability of PCA3 in the diagnosis of PCa, more rigorous studies were needed to confirm these conclusions.

Keywords: prostate cancer antigen 3, human [Supplementary Concept]; Prostate cancer, familial [Supplementary Concept]; Meta-Analysis [Publication Type]

INTRODUCTION

Prostate cancer (PCa) is a worldwide diagnosed malignant neoplasm, which has become the second mortality rate of tumors in elderly men (1-3). The clinic symptoms of PCa are mostly similar to benign prostatic hyperplasia (BPH), which makes a difficulty for clinician to accurately distinguish PCa from BPH (4). Due to lack of effective and timely diagnostic methods, the prognosis of PCa was generally poor (4). It is quiet important for clinicians to the detection of PCa at an early stage, in order to reduce the mortality of PCa, improve the survival rate and increase the opportunity of effective medical interventions (5-7).

Nowadays, serum prostate-specific antigen (PSA) is still widely used for PCa screening (5, 8). Serum PSA level has been widely used to detect PCa, which is an organ-specific antigen, but not a cancer-specific antigen (9). Several diseases, including BPH, prostatitis and PCa, might be associated with an elevated PSA level (5, 9). Though a high level of PSA is likely to be associated with PCa, the low specificity of PSA limits its use as a screening test and unnecessary biopsies (10). As a noninvasive diagnostic urine test, prostate cancer gene 3 (PCA3) is more accurate than PSA and can reduce the likelihood of false-positive results (11). Up to present, numerous individual studies have been performed to explore the diagnostic value of urine PCA3 in the management of PCa (12-18). However, these studies on the diagnostic performance of PCA3 have reported unclear or even conflicting results.

Based on a systematic review with meta—analysis, the objective of this study was to systematically collect the databases search results and perform an updated meta-analysis to assess the efficacy of diagnostic tests of PCA3 for the early detection of PCa.

MATERIALS AND METHODS

Literature search strategy

Studies were searched in the electronic databases EMBASE, PubMed and Web of Science up to February 1st, 2019. Available publications were identified using the following keywords or text words: ‘Differential Display clone 3’ or ‘DD3’ or ‘prostate cancer antigen 3’ or ‘PCA3’, ‘prostate cancer’ or ‘prostate neoplasms’ or ‘prostate carcinoma’ or ‘prostatic cancer’ or ‘prostatic neoplasm’ or ‘prostatic carcinoma’ or ‘cancer of prostate’ or ‘neoplasms of prostate’ or ‘carcinoma of prostate’, and ‘sensitivity’ or ‘specificity’ or ‘false negative’ or ‘false positive’ or ‘diagnosis’ or ‘detection’ or ‘accuracy’. For assessing all relevant studies, the most eligible literatures were retrieved. Moreover, relevant articles from reference lists of selected articles were searched to identify more relevant publications and avoid relevant information missing. No language restriction was applied.

There is no registered protocol for this systematic review. This systematic review and meta-analysis was conducted in accordance with the PRISMA guidelines, which compile guidelines for the reporting of meta-analysis of observational studies. The relevant studies included in this meta-analysis are previously published, and therefore, ethical approval and informed consent are not required.

Criteria for inclusion and exclusion of published studies

The included studies must meet the inclusion criteria: (1) A case-control, nested case-control, or cohort randomized prospective or retrospective study, (2) Evaluate the diagnostic value of PCA3 in patients with PCa, (3) Available data for extraction to calculate sensitivity, specificity and other measures, (4) When duplications or the same patients used in several publications existed, the most recent or complete study was chosen in this meta-analysis. Additionally, the major exclusion criteria were as follows: (1) No available data; (2) Non-case-control studies, case reports, letters, reviewed editorial articles, (3) Duplicated publications with previous studies.

Data extraction

The extracted appropriate information and data with a standard protocol were inspected by two researchers independently, to ensure the reliability and accuracy of the results. Moreover, the controversies were reviewed and settled through discussion by a third investigator, until all problems were finally resolved. The following information from each study were extracted: name of first author, publication date, country, ethnicity, mean age, PSA value (ng/mL), assay type, sample source, sample size, cut-off value, controls value (ng/ mL), PCa/non-PCa case, and raw data including true positive (TP), true negative (TN), false positive (FP), and false negative (FN) results.

In addition, the quality of each reference was also evaluated by two investigators independently, according to the revised QUADAS tools (19). Each domain contains seven questions, which can be answered by “yes”, “no” or “not clear” that assess the quality of included studies. An answer of “yes” means a low risk of bias, whereas “no” or “not clear” means a higher risk of bias in terms of the loss of some information from each literature.

Statistical analysis

The statistical software STATA version 12.0 (StataCorp LP, College Station, TX) was performed to conduct all statistical data in this meta-analysis, and the Spearman test was used to analyze the threshold effect or the non-threshold effect. All of the statistical tests were two-sided, and P <0.05 was considered statistically significant. The pooled sensitivity, specificity, positive likelihood ratios (LR+), negative likelihood ratios (LR−), and the diagnostic odds ratio (DOR) as well as their corresponding 95% CIs were summarized to assess the diagnostic value of PCA3 in patients with PCa. Data were visualized as forest plots and receiver operating characteristic curves (ROC). The between-study heterogeneity was evaluated by Q test and I2 statistic, and P <0.05 was deemed statistically significant. As a quantitative measurement of inconsistency across different studies, I2-square value, ranged from 0 (no observed heterogeneity) to 100% (maximal heterogeneity), was also calculated. If the heterogeneity across studies was not identified, the fixed-effects model was used. Otherwise, the random-effects model was used in the meta-analysis. In addition, the summary receiver operating characteristic (sROC) curve was generated and the area under sROC curves (AUC) was calculated both overall and the subgroup analysis. Additionally, publication bias was investigated using Deek's funnel plot asymmetry test. When the P value of the Egger test was <0.05, the statistical significance was defined. Then, we replicated the funnel plot with its “missing” counterparts around the adjusted summary estimate.

RESULTS

Studies characteristics

As shown in Figure-1, 483 records were retrieved. After screening titles and abstracts of relevant articles, 418 articles were excluded because these were not related to the inclusion criteria. Finally, 65 case-control studies published between 2003 and 2018 were included in the meta-analysis (11-18, 20-76). All of these studies were retrospective in design.

Figure 1. Flowchart of literature search and selection process.

Figure 1

The present meta-analysis included 8.139 cases and 14.116 controls from a total of 65 case-control studies about evaluating the diagnostic value of PCA3 in patients with PCa, and the detailed data of each study are listed in Table-1. Based on the studies described above, we retrieved data from 22.255 patients with PCA3 test and 5.065 patients with diagnosed PCa. All the studies presented the sensitivity, specificity, LR+, LR− and cut-off points. In these studies, these assay types, such as enzyme-linked immunosorbent assay (ELISA) and reverse transcription-polymerase chain reaction (RT-PCR), were applied to detect the expression level of PCA3. Besides, fifty studies were performed on Caucasian population, ten studies were conducted on Asian population, one study was carried out on African population, and the remaining studies involved more than one race.

Table 1. Characteristics and methodology assessment of individual studies included in the meta-analysis.

Year First author Country Ethnicity T Mean age (years) T Mean PSA (ng/mL) Assay type Sample source Cut-off value Case Control TP FP FN TN QUADA
2018 Li China/Asian Asian NR NR PCR Urine 33.9 24 53 21 9 3 42 12
2017 Sanda MG US Caucasian/Asian/African 62 (33-85) 4.8 *(0.3-460.4) PCR Urine 20 264 262 104 18 160 244 10
2017 Zhou China/Asian Asian 65.3±7.8 7.1±1.77 PCR Urine 23.5 33 89 27 48 6 41 11
2017 Rubio-Briones Spain Caucasian 61.7±6.12 4.49±1.99 PCR Urine 35 161 396 115 186 46 210 11
2017 Bernardeau S France Caucasian 66.5 5.6 PCR Urine 24 47 78 34 34 13 44 10
2017 Cao US Caucasian/African 63* (59–68) NR PCR Urine 35 77 195 50 55 27 140 12
2017 Wang China/Asian Asian 45-92 NR PCR Urine 40.38 169 425 112 81 57 344 12
2016 Abdellaoui Maane I Morocco Caucasian 52-73 6.16-15.9 PCR Tissue cutoff 1.035 64 41 48 7 16 34 11
2016 Tan China/Asian Asian 71 (60-89) 32.4 (2.5-199.7) LAMP Serum NR 89 101 76 8 13 93 10
2016 Nygård Y Norway Caucasian 64.0 (65.1*; 62.9-65.2a) 9.1 (7.2*;8.3-9.9a) PCR Urine 35 70 54 45 12 25 42 10
2015 Merola R Italy Caucasian NR NR PCR Urine 51 195 212 185 85 10 127 11
2015 Kaufmann Germany Caucasian 65 ± 5.6 (52-79) 10 ± 4.4 (4.0-25.0) PCR Urine 35 22 27 16 10 6 17 12
2015 Rubio-Briones Spain Caucasian 64(58-69) 5.2(4.3-7.2) PCR Urine 35 318 374 190 90 128 284 11
2015 Vlaeminck-Guillem V France Caucasian 64 ± 7(64*,59-69) 6.2 ± 4.3(6.6*,5-9.4) PCR Urine 35 480 535 326 155 154 380 10
2015 Coelho FF Brasil Caucasian 65.8±7.35 NR PCR Urine cutoff 0.2219 22 37 14 9 8 28 12
2015 Huang China/Asian Asian 70*(51-88) 13.67(7.98–29.02)b PCR Urine 35 112 24 90 9 22 15 11
2014 Ruffion France Caucasian 63(58-67)b 5.9(4.7-7.9)b PCR Urine 35 274 321 173 90 101 231 11
2014 Nygård Y Norway Caucasian 54.0 ± 6.4; 65.1* 9.1 ± 4.7; 7.2* PCR Urine 35 59 65 42 18 17 47 10
2014 Wei US Caucasian/Asian/African 62±8 8±14 PCR Urine 35 331 528 205 122 126 406 13
2014 Porpiglia Italy Caucasian 65 (60-70)b 6.9 (5.2-9.8)b PCR Urine 32.5 52 118 34 29 18 89 10
2014 Chevli US Caucasian 64.8± 9.2 6.4±23.3c PCR Urine 35 902 2171 478 543 424 1628 12
2013 Busetto Italy/Rome Caucasian 66.4 ± 5.3 6.8± 1.6 PCR Urine 35 68 95 46 48 22 47 11
2013 Rubio-Briones Spain Caucasian 57.5±6.2 (57*, 40-74) 4.63±2.25 (4.04*, 0.37-19.5) PCR Urine 35 105 216 82 93 23 123 10
2013 Salagierski Poland/Europe Caucasian 66.2±6.8 7.5±1.9 PCR Urine 35 24 56 18 24 6 32 11
2013 Ochiai Japan Asian 69*(42–89) 7.6 *(1.4–1908) PCR Urine 35 264 369 176 105 88 264 11
2013 Goode US Caucasian 66*(41–90) 4.8*(0.1–54.2) PCR Urine 35 95 361 48 116 47 245 11
2013 Stephan Germany/Europe Caucasian 65 *(41–81) 6.05 (0.50–19.77) PCR Urine 28 110 136 94 90 16 46 12
2012 Perdona Italy/Europe Caucasian 64.91±7.37 6.13 *(4.46–7.93)b PCR Urine 32.5 47 113 24 19 23 94 10
2012 Ng CF China/Asian Asian 71 (56-86) 20/10* (2-127) PCR Urine 35 17 24 12 2 5 22 12
2012 Crawford US Caucasian 64.4±8.6 8.0±20.0 PCR Urine 35 802 1111 389 249 413 862 12
2012 Babera Italy/Europe Caucasian 64* 9.5*(3.7-28) PCR Urine 35 110 67 36 13 74 54 10
2012 Pepe Italy/Europe Caucasian 64*(48-74) 8.9*(4.5-10) PCR Urine 35 27 47 19 27 8 20 11
2012 Pepe Italy/Europe Caucasian 62.5*(48-72) 8.5 * (3.7-24) PCR Urine 35 32 86 23 50 9 36 11
2012 Sciarra Italy/Europe Caucasian 63.7±7.24 6.98±2.86 PCR Urine 35 55 113 41 30 14 83 10
2012 Wu US Caucasian 63.5±7.4 11.0±8.5 PCR Urine 35 46 57 18 13 28 44 11
2011 Vlaeminck-Guillem V France Caucasian 63 ± 7 6.2 ± 4.3 PCR Urine 35 126 114 76 37 50 77 11
2011 Ochiai Japan Asian 66*(44-87) 7.2*(3.3-720.6) PCR Urine 35 35 67 26 17 9 50 11
2011 De La Taille A France/Germany/Europe Caucasian 63.0± 7.6 5.9 ± 2.1 PCR Urine 35 207 309 133 74 74 235 12
2011 Adam South Africa African 67(35–89) NR PCR Urine 35 44 61 34 30 10 31 11
2010 Cao China/Asian Asian NR NR PCR Urine AUC:0.73 86 45 82 24 4 21 10
2010 Roobol Netherlands/Europe Caucasian 70.07(63.7–74.0) 2.74 (0.2–23.0) PCR Urine 35 122 599 83 265 39 334 11
2010 Rigau Spain/Europe Caucasian 65.7 (44–85) 11.86 (1.5–189) PCR Urine 35 73 142 50 58 23 84 12
2010 Auprich France/Germany/Europe Caucasian 63(35–90) 7.3(1–82.7) PCR Urine 35 255 366 164 110 91 256 12
2010 Ouyang US Caucasian NR NR PCR Urine 19 43 49 31 20 12 29 10
2010 Henderson England/The Netherlands Caucasian 69.9 10.1(3.03-44.2) PCR Urine 35 6 44 5 18 1 26 11
2010 Aubin US Caucasian NR (0.30-33.9) PCR Urine 35 190 882 92 189 98 693 12
2010 Morote Spain Caucasian 64* (39–85) 6.4*(1.5–189) PCR Urine NR 83 161 75 34 8 127 11
2010 Nyberg Sweden/Europe Caucasian 63 *(57–70)b 7.9 *(5.1–12.8)b PCR Urine 35 18 44 12 24 6 20 10
2010 Shen China/Asian Asian 70.3(51–86) NR PCR Urine cutoff 0.107 35 64 22 6 13 58 10
2010 Schilling Germany/Europe Caucasian NR 7.7*(2.0–46.9) ELISA Urine 35 18 14 17 9 1 5 10
2009 Shappell US Caucasian NR NR PCR Urine 35 11 19 8 3 3 16 11
2009 Wang US Caucasian 62 ±8.3(44-86) 8.7±12.4 PCR Urine 35 87 100 46 20 41 80 10
2009 Mearini Italy/Europe Caucasian 69.1(53–83) 1.08-172.0 PCR Urine AUC: 0.814 70 26 42 0 28 26 10
2008 Haese Europe Caucasian 64.4±6.6 8.9 ± 7.6 PCR Urine 35 128 335 60 94 68 241 11
2008 Deras US/Canada Caucasian 64 (32–89) 7.8 (0.3–484) PCR Urine 35 206 357 111 93 95 264 12
2008 Nakanishi US Caucasian/African 60 (45–70) 5.7 (1.0–27.0) PCR Urine 25 40 102 25 19 15 83 12
2008 Laxman US Caucasian NR NR PCR Urine AUC:0.66 138 96 91 23 47 73 11
2007 Marks US/Canada Caucasian 64 ± 7(64*45-83) 7.4 ± 4.3(6.1*2.5-31.1) PCR Urine 35 60 166 35 46 25 120 11
2007 Van Gils MPMQ Netherlands/Europe Caucasian 64.3±7.2 7.49 ± 2.93 PCR Urine 58 174 360 113 122 61 238 12
2007 Van Gils MPMQ Netherlands/Europe Caucasian 64±7.2 8.73± 6.61 PCR Urine 43 23 44 14 9 9 35 10
2007 Van Gils MPMQ Netherlands/Europe Caucasian NR NR PCR Urine 66 23 44 15 8 8 36 10
2006 Groskopf US Caucasian 67±11 (45-93) 7.7±14.1(0.4-101.7) PCR Urine cutoff 0.05 16 52 11 11 5 41 12
2004 Tinzl Austria/Europe Caucasian 64.7 (41-89) 0.59 -1486 PCR Urine cutoff 0.5 79 122 65 29 14 93 13
2004 Fradet Canada Caucasian 64* (40-87) 0.1-144 PCR Urine cutoff 0.5 152 291 100 32 52 259 12
2003 Hessels Netherlands/Europe Caucasian NR NR PCR Urine cutoff 0.2 24 84 16 14 8 70 11
*

median;

a

95%CI;

b

IQR (interquartile range);

c

missing;

d

SEM;

AUC

area under curve;

PCA3/PSA

NA

data are not available; mean median (ranges); cutoff values were not provided because these studies found serum PCA3 has no correlation with PCa.

Quantitative synthesis results

In this meta-analysis, the random-effects model was selected to calculate the sensitivity, specificity, LR+, and LR− with corresponding 95% CIs, because of the obvious between-study heterogeneity among those studies (P <0.05). The meta—analytic results showed that the pooled overall diagnostic sensitivity, specificity, LR+, LR− and 95% CIs about PCA3 for predicting significant PCa were 0.68 (0.64-0.72), 0.72 (0.68-0.75), 2.41 (2.16-2.69), 0.44 (0.40-0.49), respectively (Figure-2). Moreover, the summary diagnostic odds ratio (DOR) and 95% CIs for the diagnostic value of PCA3 in PCa patients was 5.44 (4.53-6.53) (Figure-3). In addition, AUC and 95% CI was 0.76 (0.72-0.79) (Figure-4).

Figure 2. Flowchart of literature search and selection process.

Figure 2

Figure 3. Forest plots of summary diagnostic odds ratio of by PCA3 as a diagnostic marker for PCa in this meta-analysis. Each solid circle represents an eligible study. The size of solid circle reflects the sample size of each eligible study. Error bars represent 95% CIs.

Figure 3

Figure 4. Summary receiver operating characteristic curves of PCA3 for the diagnosis of PCa. Each solid circle represents an eligible study. The size of solid circle represents the sample size of each eligible study. The overall diagnostic efficiency is summarized by the regression curve.

Figure 4

Test of heterogeneity

The I2-square of sensitivity, specificity, LR+, LR− and DOR in this meta-analysis were as follows: 88.86%, 92.08%, 82.17%, 81.70% and 100%, which proved that the heterogeneity between eligible studies was significant. As a result, the random effects model was chosen to synthesize the relevant data mentioned above.

Publication bias

The potential publication bias of the included studies was evaluated through the Deek's funnel plot asymmetry test. The data of the slope coefficient of the regression line were symmetric, which suggested that the meta-analysis did not have a likelihood of publication bias (Figure-5).

Figure 5. Linear regression test of funnel plot asymmetry. The statistically non-significant P value of the slop coefficient indicates symmetry of the data and a low likelihood of publication bias.

Figure 5

DISCUSSION

Though PCa presents a slow progress, it has become a big threat to the health of men (4). Thus, the intervention at the early staging of PCa improves clinical prognosis. Serum PSA, DRE and transrectal ultrasound are still served as the screening of PCa in many countries and areas, which provides clinicians a low positive rate in the diagnosis of PCa (8). Among them, PSA is a serum marker widely used for screening of PCa in past years (4, 7). However, the proportion of positive biopsy is less than 50% in men with elevated serum PSA (10, 77). Therefore, the false-positive of PSA results may lead to unnecessary prostate biopsies and cause the complications of prostate biopsy (78). For these reasons, the searching for novel specific biomarkers of PCa has been attempted all the time.

In recent years, several serologic and pathologic biomarkers, with higher specificity than serum PSA, have been found to reduce unnecessary biopsy and inform the treatment (79, 80). Among them, PCA3 is one of the most valuable biomarkers in the detection of PCa (80). There are different expression of PCA3 gene in PCa tissue and other noncancerous tissue, which provides a great help for clinician to distinguish PCa from other prostatic diseases (80, 81). PCA3 gene is located on the long arm of chromosome 9 with 23kb long of nucleic acid and four exons and it cannot be translated into protein in normal cells (11, 82). In addition, it is a specific biomarker, over-expressed in more than 95% of PCa cells, so it can help to distinguish benign from cancerous prostate cells with an accuracy approaching 100% (83). Besides, PCA3 is also not affected by age, prostate volume or other prostatic diseases (81). In clinic, it is normally extracted in urine samples collected after DRE (11). And PROGENSA PCA3 assay has been already widely used to measure the level of urinary PCA3, and it can also been measured in serum and tissue samples (20, 23, 84).

Over the past years, many studies have increased to evaluate the value of PCA3 in the detection of PCa. In order to elucidate the expression differences of PCA3, meta-analysis has been updated to comprehensively and systematically investigate the diagnosis accuracy of PCA3 level in PCa patients. However, the outcomes of these studies remained inconsistent and controversial. There were several variables in these studies, such as the different ethnicities, the small sample size of individual study, the possible limited effect of individual patient data, among other factors, which could have caused the limited statistical power in the published studies. Compared with previous review and meta—analysis (85-87), this meta-analysis contains more studies for the sake of the sufficient evidence of our results. Furthermore, the publication of the previous meta-analysis might generate great influence on the results. All these factors made contributions to the development of the current meta-analysis.

Compared to a single study, meta-analysis would provide more sufficient results. Thus, we suggested that there existed stronger advantages to prove the relevance between the level of PCA3 and the diagnosis of PCa. Though it was deemed that PCA3 might be a valuable diagnostic biomarker of PCa in the previous studies, correlation between PCA3 level and the diagnosis of PCa remains unclear. Therefore, we need a better method for further analysis and elaboration about the diagnostic value of PCA3 for PCa. In the present meta-analysis, the summary DOR and 95% CIs for PCA3 was 5.44 (4.53-6.53), and AUC and 95% CIs was 0.76 (0.72-0.79). Thus, the above results revealed that PCA3 could be acceptable as a valuable biomarker to distinguish PCa patients from healthy individuals.

Overall, the sufficient statistical evidences including the large sample size were used to estimate the diagnostic value of PCA3 in the detection of PCa. However, several limitations were involved in this meta-analysis. First of all, the ethnicities involved in these studies were mainly Caucasians, However, Asian and African populations were included in relatively few studies. Thus, more attention should be paid to the influence of ethnicity. Secondly, there was a threshold effect and obvious heterogeneity in this meta-analysis, probably due to the large difference in reagent resource, patient characteristics, the assay type and the cut-off value. Moreover, the lack of sufficient data, the internal references and cut-off values were not considered in meta-regression analysis. Hence, it might reduce the reliability of our meta-analysis. In addition, more attention should be paid in further researches to the comparison of PCA3, PSA, and other biomarkers in the diagnosis of PCa. To improve reliability of the meta-analysis, well-designed studies with large sample size should be continued to evaluate the effectiveness of PCA3 in the detection of PCa in the subsequent years.

CONCLUSIONS

This meta-analysis suggested that PCA3 is acceptable as a valuable diagnostic biomarker in the management of PCa, which is a non-invasive method with the acceptable sensitivity and specificity in the diagnosis of PCa to distinguish patients from healthy individuals. To further evaluate the diagnostic value of PCA3 in patients with PCa, more well-designed studies with large sample sizes are needed to validate the effectiveness of PCA3 to differentially diagnose PCa.

Glossary

ABBREVIATIONS

PCA3

prostate cancer antigen 3;

PCa

prostate cancer;

LR+

positive likelihood ratios;

LR−

negative likelihood ratios;

CIs

confidence intervals;

sROCs

ummary receiver operating characteristic;

AUC

area under sROC curves;

BPH

benign prostatic hyperplasia;

PSA

prostate-specific antigen;

TP

true positive;

TN

true negative;

FP

false positive;

FN

false negative;

DOR

diagnostic odds ratio;

ELISA

enzyme-linked immunosorbent assay;

RT-PCR

reverse transcription-polymerase chain reaction.

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