|Year : 2014 | Volume
| Issue : 7 | Page : 218-221
Prostate cancer antigen 3 as a biomarker in the urine for prostate cancer diagnosis: A meta-analysis
Wu-Jin Xue1, Xiu-Li Ying2, Jin-Hong Jiang3, Ye-Hui Xu3
1 Department of Clinical Laboratory, The People's Hospital of Lishui City, Zhejiang Province, 323000, China
2 Department of Blood Transfusion, The People's Hospital of Lishui City, Zhejiang Province, 323000, China
3 Department of Hematology, The People's Hospital of Lishui City, Zhejiang Province, 323000, China
|Date of Web Publication||29-Nov-2014|
Department of Hematology, The People's Hospital of Lishui City, Zhejiang Province, 323000
Source of Support: None, Conflict of Interest: None
Objective: The aim of this study was to investigate the clinical value of urine Prostate cancer antigen 3 (PCA3) test in the diagnosis of prostate cancer by pooling the published data.
Methods: The clinical trials about urine PCA3 test in the diagnosis of prostate cancer were searched in the PubMed (January, 1966-July, 2014). Cochrane library (Section 3, 2013), CNKI (March, 1994-July, 2014). All relevant prospective studies of urine PCA3 test in the diagnosis of prostate cancer were screened. The aggregated sensitivity, specificity, positive likely hood ratio (+LR), negative likely hood ratio (−LR), diagnosis odds ratio (DOR) and area under the area under curve (AUC) were calculated by using Meta-disc 1.4 and STATA 11.0 statistic software.
Results: Finally, a total of 13 trials including 3245 subjects were included in this meta-analysis. The pooled sensitivity, specificity, +LR, −LR, DOR and AUC were 0.62 (95% confidence interval [CI]: 0.59-0.65), 0.75 (95% CI: 0.73-0.76), 6.16 (95% CI: 3.39-11.21), 0.50 (95% CI: 0.43-0.59), 5.49 (95% CI: 3.76-8.019) and 0.75 (95% CI: 0.71-0.78), respectively.
Conclusion: Urine PCA3 test has acceptable sensitivity and specificity in the diagnosis of prostate cancer, which can be used as non-invasive method for diagnosis of prostate cancer.
Keywords: Diagnosis, meta-analysis, prostate cancer gene 3, prostate-specific antigen
|How to cite this article:|
Xue WJ, Ying XL, Jiang JH, Xu YH. Prostate cancer antigen 3 as a biomarker in the urine for prostate cancer diagnosis: A meta-analysis. J Can Res Ther 2014;10, Suppl S3:218-21
|How to cite this URL:|
Xue WJ, Ying XL, Jiang JH, Xu YH. Prostate cancer antigen 3 as a biomarker in the urine for prostate cancer diagnosis: A meta-analysis. J Can Res Ther [serial online] 2014 [cited 2020 Nov 24];10:218-21. Available from: https://www.cancerjournal.net/text.asp?2014/10/7/218/145881
Wu-Jin Xue and Xiu-Li Ying contribute equally to this work
| > Introduction|| |
Prostate cancer is the second most frequently diagnosed carcinoma and the sixth leading cause of cancer-related death in males, accounting for 14% (903,500) of the total new cancer cases and 6% (258,400) of the total cancer deaths in males in 2008.  Early screening of prostate cancer depends on an elevated prostate-specific antigen (PSA) level and an abnormal digital rectal examination. And if the abnormal of the PSA or digital rectal examination were found, the further prostate biopsy is necessary.  The most frequently used biomarker for prostate cancer today is the serum level of PSA. However, the PAS level can be affected by many other diseases, such as benign prostatic hyperplasia, acute prostatitis and et al., which could elevate the PSA serum level.
Prostate cancer antigen 3 (PCA3, also referred to as DD3) is a gene that expresses a non-coding RNA. The published articles showed that the PCA3 is only expressed in human prostate tissue, and it is highly over-expressed in patients with prostate cancer. For its restricted expression profile, the PCA3 level was deemed as a promising biomarker for prostate cancer.
| > Methods|| |
0 Search strategy
The search databases were PubMed (January, 1966-July, 2014), Cochrane Library (Section 3, 2013), CNKI (March, 1994-July, 2014). All relevant articles about serum level PCA3 in the diagnosis of prostate cancer were reviewed. The search items were as follows: "PCA 3/PCA3/DD3", "prostate cancer", "prostate carcinoma", "diagnosis".
The inclusion criteria were as follows: The study design was prospective; the patients were confirm by gold standard such as pathology or cytology; enough data (true positive, false positive false negative and true negative) can be drawn from the original study.
The general characteristics and detailed data for diagnosis were extracted separated by two reviewers (Wu-Jin Xue and Xiu-LiYing) and finally checked by a third reviewer (Ye-Hui Xu). The general characteristics including the name of the first author, the year of publication, the country the experiment was done and the cut off vale of PCA3. The detailed information for diagnosis was the true positive, false positive false negative and true negative in the individual study.
STATA/SE 11.0 (StataCorp LP, http://www.stata.com) was used to do a statistical analysis. Statistical heterogeneity across trials was evaluated using Chi-square test,  and the inconsistency was calculated by I 2 .  If heterogeneity was found (P < 0.05 or I 2 > 50%), the random effect method (Dersimonian-Laird method) was used to pool the data and subgroups analysis was done for further evaluation. Inversely, without significant heterogeneity, fixed-effect method was used.
| > Results|| |
0 The general characteristic of included trials
By searching the databases, 521 potential articles were fond initially. After reviewing the title and abstract, 475 papers were excluded for not suitable for the inclusion criteria with 46 articles left. And finally 13 trials ,,,,,,,,,,,, were included in this meta-analysis by reviewing the full text. Of the included 13 trials, 8 were from USA, 3 from The Netherlands, 1 from Germany and 1 from China. The general characteristics were summarized in [Table 1].
Pooled sensitivity and specificity
The Chi-square test showed that a significant heterogeneity were existed across the studies with P = 0.00 in the effect size of sensitivity and specificity. Thus, the random effects model was used to aggregate the sensitivity and specificity. The pooled data showed the sensitivity and specificity were 0.62 (95% confidence interval [CI]: 0.59-0.65), and 0.75 (95% CI: 0.73-0.76) respectively [Figure 1].
Pooled positive likely hood ratio and negative likely hood ratio
Significant heterogeneity was found in the pooled analysis of positive likely hood ratio (+LR) and negative likely hood ratio (−LR) with an I2 of 82.6% and 68.3% across the studies. The data were aggregated by random effect model with a pooled + LR of 2.64 (95% CI: 2.10-3.32) and (95% CI: 0.43-0.59) respectively [Figure 2].
|Figure 2: Forest plot of pooled positive likely hood ratio and negative likely hood ratio|
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Pooled diagnosis odds ratio
The pooled diagnosis odds ratio was 5.49 (95% CI: 3.76-8.01) by using random effect model [Figure 3].
Summary receiver operating characteristic curve
The pooled data demonstrated that the area under curve (AUC) was 0.75 with its 95% CI of 0.71-0.78, indicating a moderate level of accuracy [Figure 4].
|Figure 4: The area under curve of receiver operating characteristic curve for diagnosis of prostate cancer by prostate cancer antigen 3 array|
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| > Discussion|| |
The most frequently used biomarker for prostate cancer today is the serum level of PSA. However, the clinical value for diagnoses was not conclusive. A large US-based randomized trial on the efficacy of PSA testing in reducing mortality from prostate cancer found no benefit  whereas another similar European-based trial found a modest benefit.  Differences in study design, sample size (statistical power), follow up, and possible contamination of controls may have contributed to the different findings between these studies. Thus, other biomarkers with highly sensitivity and specificity were needed in screening the prostate cancer.
Prostate cancer antigen 3 (PCA3, also referred to as DD3) is a non-coding RNA, which was only expressed in human prostate tissue and is highly over-expressed in patients with prostate cancer. Several published articles demonstrated that the serum level of PCA3 has been shown to be a useful biomarker to predict the presence of prostate malignancy. , However, the diagnosis value of sensitivity and specificity was not consistent with each other. So, we performed this meta-analysis in order to further evaluate the clinical value of serum level of PCA3 in the diagnosis of prostate cancer. We search the PubMed (January, 1966-July, 2014), Cochrane Library (Section 3, 2013), CNKI (March, 1994-July, 2014) data bases and finally included 13 trials about the PCA3 in the diagnosis of prostate cancer. Of the included 13 studies, the sensitivity and specificity range from 0.47-0.81 to 0.56-0.89. In this meta-analysis the pooled sensitivity and specificity were 0.62 (95% CI: 0.59-0.65), and 0.75 (95% CI: 0.73-0.76) respectively. The results indicated that if 100 patients diagnosis with prostate cancer by serum PCA3, about 25 patients were not real prostate cancer patients with a false positive rate of 25%. And if 100 exact prostate cancer patients were tested for serum PCA3, about 62 patients were diagnosis of prostate cancer, and other 38 was deemed as normal with a false negative of 38%.
For receiver operating characteristic (ROC), the best possible prediction method would yield a point in the upper left corner or coordinate (1,1) of the ROC space, representing 100% sensitivity (no false negatives) and 100% specificity (no false positives). The area under the curve is equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one.  In this meta-analysis, AUC was 0.75 with its 95% CI of 0.71-0.78, indicating a moderate level of accuracy.
Although the pooled data indicated that the urine PCA3 test has acceptable sensitivity and specificity in diagnosis of prostate cancer, which can be used as non-invasive method for diagnosis of prostate cancer, the small number trials included in this meta-analysis and significant heterogeneity across the studies made the conclusion conservative.
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[Figure 1], [Figure 2], [Figure 3], [Figure 4]