UROLOGICAL ONCOLOGY Urine Biomarkers for the Diagnosis of Bladder Cancer: A Network Meta-Analysis Ying Dong1, Ting Zhang2, Xining Li2, Feng Yu2, Hongwei Yu 2, Shenwen Shao2* Purpose: To identify effective urine biomarkers for bladder cancer diagnosis. Materials and Methods: This meta-analysis was conducted following the guidelines of the Meta-Analyses (PRIS- MA) statement. Relevant studies were searched from the PubMed, Embase, and Cochrane Library databases. Heterogeneity tests were performed using Q statistics and I2 tests to determine the use of the random or fixed ef- fects model. A direct comparison meta-analysis and network meta-analysis were conducted. The effect values are presented as odds ratios and 95% confidence intervals. Sensitivity analysis and consistency tests were performed. Results: Fifty-eight studies with 12,038 participants were included. Direct comparison meta-analysis showed sta- tistically significant differences in bladder cancer antigen (BTA) trak vs. nuclear matrix protein 22 (NMP22), BTA stat vs. urine cytology (UC), and fluorescence in situ hybridization (FISH) vs. UC, among the sensitivity indicators. Among the specificity indicators, there were statistically significant differences in BTA trak vs. UC, ImmunoCyt (immunocyte) vs. NMP22, and BTA stat vs. FISH. Among the positive predictive indicators, NMP22 vs. UC, BTA stat vs. UC, and FISH vs. NMP22 showed statistically significant differences. Among the negative predictive indicators, the differences in FISH vs. UC, FISH vs. NMP22, and hyaluronidase 1 (HYAL-1) vs. UC were statistically significant. Among the accuracy indicators, FISH vs. NMP22, FISH vs. UC, and HYAL-1 vs. UC showed statistically significant differences. Network meta-analysis showed that HYAL-1, urothelial carcinoma associated 1 (UCA1) and survivin had the highest sensitivity, while UC had the lowest sensitivity. The specificity of UC, FISH, and HYAL-1 was the highest, while that of UCA1 was the lowest. In terms of positive predictive indicators, UC, FISH, and HYAL-1 had the highest positive predictive value, while the BTA group had the lowest positive predictive value. In terms of negative predictive indicators, HYAL-1, UCA1, and survivin had the highest negative predictive value, while UC had the lowest negative predictive value. In terms of accuracy indicators, HYAL-1, UCA1, and survivin had the highest accuracy, while UC had the lowest accuracy. Conclusion: HYAL-1 and survivin are suitable urine biomarkers for bladder cancer diagnosis. Keywords: bladder cancer; urine biomarker; network meta-analysis; diagnostic value INTRODUCTION Bladder cancer (BC) is a common malignancy of the genitourinary system, which is characterized by urine occult blood, lower back pain, and painful urination(1). BC is generally induced by family history, bladder infection, smoking, radiotherapy, and chemical exposure (2,3). The main BC types include transitional cell carcinoma, adenocarcinoma, and squamous cell carcinoma (4). BC patients in different stages may be treated with surgery, immunotherapy, chemotherapy, or radiotherapy, with five-year survival rates of 77% in the United States(5). BC is more likely to occur in males than in females, and often occurs in people be- tween the ages of 65–85 years(6). In 2015, BC affected approximately 3.4 million people and was responsible for 188,000 deaths globally(7). Therefore, BC should be further studied to improve its diagnosis and treatment. With the development of molecular biology techniques, 1Schools of Medicine and Nursing Sciences, Huzhou University, Huzhou Central Hospital, Huzhou, Zhejiang, 313000, China. 2Schools of Medicine and Nursing Sciences, Huzhou University, Huzhou, Zhejiang, 313000, China. *Correspondence: Schools of Medicine and Nursing Sciences, Huzhou University, No.1 Xueshi Road, Wuxing District, Huzhou, 313000, China. Tel: +86-15857277362. Email: jiajitouwen6@163.com Received May 2020 & Accepted September 2021 new BC detection methods have arisen in recent years. Bladder tumor antigen (BTA) and fluorescence in situ hybridization (FISH) are the primary urine biomark- ers for noninvasive screening and monitoring of BC in clinical research(8), however, the sensitivity and speci- ficity of urine biomarkers for BC diagnosis vary widely among different studies. For example, nuclear matrix protein-22 (NMP22) and fibronectin have greater sensi- tivity than voided urine cytology (UC) and urinary BTA, while voided UC and NMP22 have superior specifici- ties(9). Urinary BTA has higher sensitivity and speci- ficity for screening low- grade and low-stage BC, and thus, may be more valuable for BC diagnosis than the BTA stat test and NMP22(10). UC is highly specific but poorly sensitive for detecting BC, and FISH combined with UC has good sensitivity and specificity in evaluat- ing BC(11). Moreover, direct comparison meta-analyses have explored the diagnostic value of urine biomarkers Urology Journal/Vol 18 No. 6/ November-December 2021/ pp. 623-632. [DOI: 10.22037/uj.v18i.6254] in BC(12,13). Chou et al. found that urine biomarkers miss a considerable fraction of BC patients, and their accu- racies are low for low-grade and low-stage tumors(12). Guo et al. revealed that the UC test may have a higher Q index, specificity, negative likelihood ratio (LR), posi- tive LR, area under the curve, and diagnostic odds ratio in comparison to the BTA stat test, while the sensitivity of the BTA stat test is superior to that of the UC test(13). However, no relevant network meta-analyses have been published to date. Therefore, it is necessary to carry out Urine biomarkers for diagnosing BC-Dong et al. Figure 2. The network diagram. UC: urine cytology; FISH: fluorescence in situ hybridization; NMP-22: nuclear matrix protein 22; UCA1:urothelial carcinoma associated-1; HYAL-1: hyaluronidase 1; UC: urine cytology; ImmunoCyt: immunocyte; BTA: bladder can- cer antigen. Figure 1. The literature screening processes. Urological Oncology 624 a network meta-analysis of the literature related to the accuracy of urine biomarkers in BC diagnosis using cystoscopy or pathological examination as the gold standards. This study may clarify the diagnostic values of several urine biomarkers for BC and provide a scien- tific basis for future clinical treatment, including hyalu- ronidase 1 (HYAL-1), urothelial carcinoma associated 1 (UCA1), survivin, immunocyte (ImmunoCyt), BTA stat, NMP22, BTA trak, UC, and FISH.. MATERIALS AND METHODS This meta-analysis was conducted following the guide- lines of the Meta-Analyses (PRISMA) statement (14). Search strategy From PubMed (http://www.ncbi.nlm.nih.gov/pubmed), Embase (http://www.embase.com), and Cochrane Li- brary (http://www.cochranelibrary.com) electronic lit- erature databases, the English literature on urine bio- markers in BC diagnosis (published before September 30, 2020) were systematically retrieved. The searching words were "bladder urothelial cell carcinoma" OR "carcinoma of urinary bladder" OR "bladder cancer” OR “carcinoma of bladder" OR "bladder carcinoma" OR "bladder tumor" AND "bladder cancer antigen" OR “BTA” OR "BTA stat" OR "BTA trak", “FISH” OR "fluorescence in situ hybridization", “cytology” OR “cytological”, “ImmunoCyt” OR “immunocyte”, "Nuclear Matrix Protein 22" OR “NMP22”, “HYAL1”, OR “hyaluronidase”, “survivin”, “urothelial carcino- ma associated 1” OR “UCA1” AND “diagnostic” OR “diagnosis” OR “sensitiveness” OR “susceptibility” OR “sensitivity” OR “specificity” OR “ROC”. Fur- thermore, the reference lists of reviews and retrieved articles were manually searched for additional records. Inclusion and exclusion criteria Strict inclusion criteria were established, and the in- cluded literature were selected based on the following criteria: (1) the study was a published English literature on the diagnostic value of urinary biomarkers in pa- tients with suspected bladder cancer (including prima- ry bladder cancer, and recurrent or metastatic bladder cancer); (2) the cases were pathologically confirmed by cystoscopy or surgically proven bladder cancer pa- tients; (3) the control group included healthy controls and other benign tumor participants; (4) the study in- volved at least two BTA, FISH, UC, ImmunoCyt, NMP22, HYAL-1, survivin, and UCA1, and the true positive (TP) number, false positive (FP) number, false negative (FN) number, and true negative (TN) number of diagnostic tests could be provided or obtained ac- cording to the relevant known indicators. The exclusion criteria were as follows: (1) the study contained incomplete data and could not be used for statistical analysis; (2) the study was comment, review, letter, etc.; (3) for repeatly published studies or stud- ies involving the same population data, only the most recent study or the study with the most complete infor- mation would be included; (4) studies with fewer than 10 patients were excluded in order to reduce the bias caused by chance. Data extraction Two investigators independently extracted relevant data from the included literature, and the extracted con- tents included: the first author of the literature, publi- cation year, study year, study country, total number of included people, age of the subjects, number of men, diagnostic methods of bladder cancer, and number of TP, FP, FN, and TN. The Quality Assessment of Diagnostic Accuracy Stud- ies (QUADAS) tool was used to evaluate literature quality, and 14 items were evaluated according to three criteria: "yes" (meeting this standard), "no" (not meet- ing or not mentioned), and "unclear" (partially meeting or not getting information obtained from the literature) (15). In case of any dispute in the data extraction and quality evaluation processes, a group discussion would be held, and a consistent result would be obtained after communicating with the third investigator. Statistical analysis The Meta package (version 3.4.3, http://cran.r-project. org/webpackages/meta/index.html) in R(16) was used for direct comparison. Sensitivity (Se), specificity (Sp), positive predictive value (PPV), negative predictive value (NPV), and accuracy were used to evaluate the efficacies of the two diagnostic methods, and the odds ratio (OR) and 95% confidence interval (CI) were used as the effect values of the results. Before data consoli- dation, the research data were tested for heterogeneity, and the I2 statistic was used for the heterogeneity test. If the heterogeneity test showed a statistical difference (I2 > 50%), the random effects model was used to calcu- late the combined effect value. Alternatively, the fixed effects model was selected to merge data (I2 ≤ 50%) (17). Egger’s test was used to evaluate whether there was publication bias among the included studies. Network meta-analysis was conducted using the net- meta package (version 3.4.3, https://cran.r-project.org/ web/packages/netmeta/index.html) in R(18). The het- erogeneity of the whole network meta-analysis was Table 1. The comprehensive comparison of sensitivity BTA stat 0.84[0.30;2.38] BTA trak 1.08[0.67;1.74] 1.28[0.44;3.73] FISH . 0.23[0.07;0.71] 0.27[0.06;1.19] 0.21[0.07;0.66] HYAL-1 . 0.80[0.38;1.69] 0.95[0.28;3.22] 0.74[0.35;1.57] 3.54[0.97;12.92] ImmunoCyt . 1.10[0.75;1.62] 1.31[0.47;3.60] 1.02[0.65;1.61] 4.84[1.56;15.02] 1.37[0.66;2.83] NMP22 . 0.60[0.31;1.14] 0.71[0.22;2.26] 0.55[0.28;1.09] 2.62[0.88;7.81] 0.74[0.30;1.83] 0.54[0.28;1.04] Survivin . 2.69[1.90;3.81] 3.19[1.16;8.79] 2.49[1.72;3.59] 11.82[3.98;35.14] 3.34[1.65;6.77] 2.44[1.76;3.39] 4.52[2.52;8.10] UC ] 0.47[0.13;1.72] 0.55[0.11;2.78] 0.43[0.12;1.60] 2.05[0.39;10.82] 0.58[0.14;2.45] 0.42[0.12;1.55] 0.78[0.20;3.13] 0.17[0.05;0.61] UCA1 Abbreviations: UC, urine cytology; FISH, fluorescence in situ hybridization; NMP-22, nuclear matrix protein 22; UCA1:urothelial car- cinoma associated-1; HYAL-1, hyaluronidase 1; UC, urine cytology; ImmunoCyt, immunocyte; BTA, bladder cancer antigen. Urine biomarkers for diagnosing BC-Dong et al. Vol 18 No 6 November-December 2021 625 Urological Oncology 626 calculated using Cochran’s Q statistic, and the mod- el was selected based on the degree of heterogeneity (fixed effect model was used for the combination if the P-values of the Q statistic were all > 0.05. Otherwise, a random-effect model was used for the combination) (19). The Mantel-Haenszel method was used for the fixed effect model, and the DerSimonian-Laird method was utilized for the random effect model. The good and bad order of each intervention was ranked according to the P-score(20). The higher the P-score, the better the diag- nostic effect. Sensitivity analysis of the P-score was performed using random effect and fixed effect mod- els. In the test of consistency, all the P-values of the node-splitting analysis were used to judge the results of indirect and direct comparisons. If P > 0.05, it was considered consistent with the consistency hypothesis. RESULTS Eligible studies The literature retrieval results and literature screening processes are presented in Figure 1. A total of 5,001 English articles were retrieved from PubMed (2103), Embase (2209), and Cochrane Library (689) databases using previously developed retrieval strategies. After 1812 duplicates were removed, 3189 studies remained. Then, 2960 articles were filtered out by browsing the title and abstract. From the remaining 229 studies, 171 studies (26 case series/reports, 28 letters/comments, 31 article reviews/meta-analysis, 9 repeated articles, and 77 researches with only a diagnostic method) were screened out after reading the full text. Finally, 58 stud- ies were included in this meta-analysis(21-78). Study characteristics The 58 literatures were published from 1998 to 2020. The research locations include the United States, Chi- na, Germany, Spain, Italy, and others. A total of 12,038 participants were enrolled in this study. In terms of the age index, the participants were predominantly mid- dle-aged and elderly. In terms of gender, there were more male participants than female participants. Bio- markers mainly included BTA, FISH, UC, ImmunoCyt, NMP22, HYAL-1, survivin, and UCA1. In the BTA trak group, the TP, FP, FN, and TN numbers were 98, 68, 53, and 188, respectively. In the BTA stat group, the TP, FP, FN, and TN numbers were 1177, 573, 571, and 1631, respectively. In the ImmunoCyt group, the TP, FP, FN, and TN numbers were 226, 69, 90, and 171, respectively. In the FISH group, the TP, FP, FN, and TN numbers were 964, 368, 421, and 2878, respec- tively. In the NMP 22 group, the TP, FP, FN, and TN numbers were 1551, 731, 616, and 3612, respectively. In the UC group, the TP, FP, FN, and TN numbers were 2034, 935, 1592, and 6412, respectively. In the HYAL- 1 group, the TP, FP, FN, and TN numbers were 205, 21, 13, and 1239, respectively. In the survivin group, the TP, FP, FN, and TN numbers were 616, 152, 48, and 509, respectively. In the UCA1 group, the TP, FP, FN, and TN numbers were 153, 16, 20, and 110, respective- ly. (Supplementary Table 1). Quality evaluation of the results showed that the overall quality of the literature was relatively high (Supple- mentary Table 2). However, part of the literatures did not mention “Did the spectrum of patients represent the patients who will receive the test in practice,” and all the literatures did not mention “Were uninterruptable/ intermediate test results reported.” In other projects, most studies showed a low risk of bias. Direct comparison meta-analysis First, the heterogeneity test of sensitivity, specificity, positive predictive indicators, negative predictive in- dicators, and accuracy were performed, and suitable effect models were utilized (Supplementary Table 3 and Supplementary Figures 1–5). For instance, in the direct comparison meta-analysis, the sensitivity of BTA BTA stat 1.16[0.44;3.03] BTA trak 0.34[0.20;0.58] 0.30[0.11;0.82] FISH 0.26[0.02;2.81] 0.22[0.02;2.85] 0.76[0.07;8.37] HYAL-1 0.59[0.27;1.30] 0.51[0.16;1.66] 1.73[0.80;3.75] 2.29[0.19;27.17] ImmunoCyt 0.84[0.57;1.23] 0.72[0.28;1.84] 2.44[1.49;4.01] 3.23[0.30;34.91] 1.41[0.66;3.02] NMP22 1.29[0.53;3.14] 1.11[0.32;3.92] 3.76[1.45;9.73] 4.96[0.52;47.49] 2.17[0.71;6.67] 1.54[0.63;3.75] Survivin 0.21[0.14;0.31] 0.18[0.07;0.47] 0.62[0.40;0.94] 0.81[0.08;8.69] 0.36[0.17;0.75] 0.25[0.18;0.36] 0.16[0.07;0.39]UC 2.01[0.31;13.20] 1.74[0.22;13.76] 5.87[0.89;38.75] 7.75[0.39;155.69] 3.39[0.47;24.64] 2.40[0.37;15.63] 1.56[0.20;11.94] 9.54 UCA1 [1.52;60.04] Table 2. Comprehensive comparison of specificity. UC, urine cytology; FISH, fluorescence in situ hybridization; NMP-22, nuclear matrix protein 22; UCA1:urothelial carcinoma associat- ed-1; HYAL-1, hyaluronidase 1; UC, urine cytology; ImmunoCyt, immunocyte; BTA, bladder cancer antigen. UC, urine cytology; FISH, fluorescence in situ hybridization; NMP-22, nuclear matrix protein 22; UCA1: urothelial carcinoma associat- ed-1; HYAL-1, hyaluronidase 1; UC, urine cytology; ImmunoCyt, immunocyte; BTA, bladder cancer antigen. BTA stat 1.28[0.59;2.77] BTA trak 0.48[0.31;0.73] 0.37[0.16;0.85] FISH . 0.26[0.03;2.13] 0.20[0.02;1.88] 0.55[0.07;4.52] HYAL-1 0.58[0.31;1.05] 0.45[0.18;1.14] 1.20[0.66;2.20] 2.20[0.25;19.13] ImmunoCyt 0.94[0.70;1.26] 0.74[0.35;1.55] 1.96[1.32;2.92] 3.60[0.44;29.31] 1.63[0.91;2.93] NMP22 1.30[0.64;2.62] 1.02[0.37;2.78] 2.71[1.27;5.79] 4.97[0.67;36.75] 2.26[0.94;5.43] 1.38[0.68;2.79] Survivin 0.45[0.33;0.62] 0.36[0.17;0.76] 0.95[0.68;1.33] 1.74[0.22;14.11] 0.79[0.45;1.40] 0.48[0.37;0.64] 0.35[0.18;0.70] UC 2.36[0.43;13.07] 1.85[0.29;11.71] 4.93[0.89;27.42] 9.04[0.62;132.43] 4.10[0.69;24.26] 2.51[0.46;13.82] 1.82[0.29;11.22] 5.19 UCA1 [0.96;27.92] Table 3. The comprehensive comparison of positive predictive. Urine biomarkers for diagnosing BC-Dong et al. Vol 18 No 6 November-December 2021 627 stat vs. FISH, BTA stat vs. NMP22, BTA trak vs. UC, BTA stat vs. UC, and FISH vs. ImmunoCyt showed significant heterogeneity (I2 > 50%); thus, the random effect model was adopted. There were no significant differences in the sensitivity of BTA stat vs. Immu- noCyt, BTA trak vs. NMP22 (I2 < 50%); therefore, a fixed-effect model was used. The results of the meta-analysis showed that there were statistically significant differences between BTA train- ing and NMP22 (NMP22 was superior to BTA trak), BTA stat vs. UC (BTA stat was superior to UC), FISH vs. UC (FISH was superior to UC), NMP22 vs. UC (NMP22 was superior to UC), HYAL-1 vs. UC (HYAL- 1 was superior to UC), survivin vs. HYAL-1 (HYAL- 1 was superior to survivin), survivin vs. UC (survivin was superior to UC), and UCA1 vs. UC (UCA1 was superior to UC) among the sensitivity indicators (P < 0.05). Among the specificity indicators, there were statistically significant differences in BTA training vs. UC (UC was superior to BTA trak), ImmunoCyt vs. NMP22 (ImmunoCyt was superior to NMP22), BTA stat vs. FISH (FISH was superior to BTA stat), BTA stat vs. BTA trak (BTA group was superior to BTA stat), BTA stat vs. UC (UC was superior to BTA stat), NMP22 vs. UC (UC was superior to NMP22), HYAL-1 vs. UC (UC was superior to HYAL-1), survivin vs. UC (UC was superior to survivin), and UCA1 vs. UC (UC was superior to UCA1) (P < 0.05). Among the positive predictive indicators, NMP22 vs. UC (UC was superior to NMP22), BTA stat vs. UC (UC was superior to BTA stat), FISH vs. NMP22 (FISH was superior to NMP22), ImmunoCyt vs. NMP22 (ImmunoCyt was superior to NMP22), BTA trak vs. UC (UC was superior to BTA track), UCA1 vs. UC (UC was superior to UCA1), and survivin vs. UC (UC was superior to survivin) showed statistically significant differences (P < 0.05). Among the negative predictive indicators, FISH vs. UC (FISH was superior to UC), FISH vs. NMP22 (FISH was supe- rior to NMP22), HYAL-1 vs. UC (HYAL-1 was superi- or to UC), survivin vs. HYAL-1 (HYAL-1 was superior to survivin), and survivin vs. UC (survivin was superior to UC) were statistically significant (P < 0.05). Among the accuracy indicators, FISH vs. NMP22 (FISH was superior to NMP22), FISH vs. UC (FISH was superi- or to UC), HYAL-1 vs. UC (HYAL-1 was superior to UC), survivin vs. HYAL-1 (HYAL-1 was superior to survivin), and survivin vs. UC (survivin was superior to UC) showed statistically significant differences (P < 0.05). There are no significant differences between the other groups (Supplementary Table 3). Egger’s test showed that there was no significant publication bias among the groups. Network meta-analysis Network meta-analysis was performed using the net- meta package, and a network diagram was constructed (Figure 2); a total of nine biomarkers are included in this network meta-analysis: HYAL-1, UCA1, survivin, ImmunoCyt, BTA stat, NMP22, BTA trak, UC, and FISH. Among all the indicators, the heterogeneity of the network meta-analysis was calculated using Q sta- tistics. Based on the results, a random effects model was used for meta-analysis consolidation. The results of the network meta-analysis are listed in Tables 1–6. In terms of sensitivity, HYAL-1, UCA1, and survivin were the most sensitive groups in terms of P-score, and UC was the least sensitive group. Moreo- ver, HYAL-1, UCA1, survivin, ImmunoCyt, BTA stat, NMP22, and FISH were statistically different from UC, and BTA stat was statistically different from HYAL-1. In terms of specificity, UC, FISH, and HYAL-1 were the highest, and that of UCA1 was the lowest. UC and FISH were statistically different from BTA stat. BTA, ImmunoCyt , NMP22, and FISH were statistically dif- BTA stat 0.95[0.46;1.95] BTA trak 0.85[0.62;1.18] 0.90[0.43;1.88] FISH 0.25[0.11;0.54] 0.26[0.09;0.73] 0.29[0.13;0.64] HYAL-1 0.81[0.48;1.35] 0.85[0.36;1.98] 0.95[0.57;1.58] 3.27[1.32;8.10] ImmunoCyt 1.00[0.77;1.29] 1.05[0.52;2.12] 1.17[0.86;1.59] 4.05[1.84;8.93] 1.24[0.75;2.05] NMP22 0.62[0.39;0.97] 0.65[0.29;1.46] 0.72[0.45;1.17] 2.51[1.16;5.44] 0.77[0.41;1.44] 0.62[0.39;0.97] Survivin 1.31[1.04;1.66] 1.39[0.69;2.80] 1.54[1.20;1.98] 5.34[2.49;11.45] 1.63[1.00;2.66] 1.32[1.06;1.64] 2.13[1.42;3.20] UC 0.39[0.15;0.99] 0.41[0.13;1.28] 0.46[0.18;1.16] 1.58[0.49;5.15] 0.48[0.17;1.35] 0.39[0.15;0.99] 0.63[0.24;1.69] 0.30 UCA1 [0.12;0.73] Table 4. Comprehensive comparison of negative predictive. UC, urine cytology; FISH, fluorescence in situ hybridization; NMP-22, nuclear matrix protein 22; UCA1: urothelial carcinoma associat- ed-1; HYAL-1, hyaluronidase 1; UC, urine cytology; ImmunoCyt, immunocyte; BTA, bladder cancer antigen. BTA stat 0.91[0.45;1.82] BTA trak 0.74[0.52;1.06] 0.82[0.40;1.69] FISH 0.22[0.09;0.53] 0.24[0.08;0.72] 0.30[0.12;0.72] HYAL-1 0.77[0.44;1.35] 0.85[0.37;1.98] 1.04[0.60;1.80] 3.49[1.29;9.43] ImmunoCyt 1.01[0.76;1.35] 1.12[0.57;2.21] 1.36[0.97;1.90] 4.57[1.91;10.96] 1.31[0.76;2.26] NMP22 0.46[0.28;0.76] 0.51[0.23;1.15] 0.62[0.37;1.05] 2.09[0.90;4.89] 0.60[0.30;1.20] 0.46[0.28;0.75] Survivin 1.03[0.79;1.34] 1.14[0.58;2.23] 1.38[1.05;1.81] 4.65[2.00;10.82] 1.33[0.79;2.26] 1.02[0.80;1.29] 2.22[1.41;3.50] UC 0.56[0.21;1.50] 0.62[0.19;1.98] 0.75[0.28;2.02] 2.53[0.71;9.02] 0.73[0.24;2.15] 0.55[0.21;1.47] 1.21[0.42;3.47] 0.54 UCA1 [0.21;1.41] UC, urine cytology; FISH, fluorescence in situ hybridization; NMP-22, nuclear matrix protein 22; UCA1: urothelial carcinoma associat- ed-1; HYAL-1, hyaluronidase 1; UC, urine cytology; ImmunoCyt, immunocyte; BTA, bladder cancer antigen. Table 5. Comprehensive comparison of accuracy. Urine biomarkers for diagnosing BC-Dong et al. Urological Oncology 628 BTA stat 0.95[0.46;1.95] BTA trak 0.85[0.62;1.18] 0.90[0.43;1.88] FISH 0.25[0.11;0.54] 0.26[0.09;0.73] 0.29[0.13;0.64] HYAL-1 0.81[0.48;1.35] 0.85[0.36;1.98] 0.95[0.57;1.58] 3.27[1.32;8.10] ImmunoCyt 1.00[0.77;1.29] 1.05[0.52;2.12] 1.17[0.86;1.59] 4.05[1.84;8.93] 1.24[0.75;2.05] NMP22 0.62[0.39;0.97] 0.65[0.29;1.46] 0.72[0.45;1.17] 2.51[1.16;5.44] 0.77[0.41;1.44] 0.62[0.39;0.97] Survivin 1.31[1.04;1.66] 1.39[0.69;2.80] 1.54[1.20;1.98] 5.34[2.49;11.45] 1.63[1.00;2.66] 1.32[1.06;1.64] 2.13[1.42;3.20] UC 0.39[0.15;0.99] 0.41[0.13;1.28] 0.46[0.18;1.16] 1.58[0.49;5.15] 0.48[0.17;1.35] 0.39[0.15;0.99] 0.63[0.24;1.69] 0.30 UCA1 [0.12;0.73] ferent from UC. In terms of positive predictive indica- tors, UC, FISH, and HYAL-1 had the highest positive predictive value, while the BTA group had the lowest positive predictive value. HYAL-1 and UC were sta- tistically different from FISH results. Furthermore, the differences in BTA stat/FISH and UC, FISH and NMP22, NMP22 and UC comparison groups were sta- tistically significant. In terms of negative predictive indicators, HYAL-1, UCA1, and survivin had the high- est negative predictive value, while UC had the low- est negative predictive value. There was a significant difference between FISH and UC groups. In terms of accuracy indicators, HYAL-1, UCA1, and survivin had the highest accuracy, while UC had the lowest accura- cy. The differences between FISH and BTA stat were statistically significant. Additionally, the differences between FISH/NMP22 and UC were statistically signif- icant. There were no statistically significant differences among the groups for the other indicators. Sensitivity analysis In the sensitivity analysis, the random effect model and fixed effect model of the P-score were calculated. The results show that the order is basically identical, prov- ing that the results are relatively stable (Table 6). Consistency test Combined with the P-values of the node-splitting anal- ysis, the results of indirect and direct comparisons were determined. The results showed that most results were > 0.05. These findings suggest that the results are rela- tively stable (Supplementary Tables 4–8). DISCUSSION In this meta-analysis, 58 eligible studies were selected. Quality evaluation showed that the overall quality of the included studies was relatively high. Network me- ta-analysis revealed that HYAL-1, UCA1, and survivin were the most sensitive groups, and UC was the least sensitive group. In terms of specificity, the specificity of UC, FISH, and HYAL-1 was the highest, and that of UCA1 was the lowest. UC, FISH, and ImmunoCyt had the highest positive predictive value, while the BTA trak had the lowest positive predictive value. Moreover, HYAL-1, UCA1, and survivin had the highest negative predictive value, whereas UC had the lowest negative predictive value. Additionally, HYAL-1, UCA1, and survivin had the highest accuracy, while UC had the lowest accuracy. Sensitivity analysis and consistency tests suggest that the results are relatively stable. HYAL-1 has been reported to play an important role in tumor growth and progression. Kramer et al. found that HYAL-1 expression predicted BC metastasis dis- ease-specific survival(79). HYAL-1 and -2 are presumed to constitute the major hyaluronidases involved in the catabolism of hyaluronic acid (HA) in somatic tissues. A previous study indicated that HAase mRNA exhib- ited superior sensitivity (86.67%) over UC (38.33%) with specificities of 97.5% and 100%, respectively, in BC detection(68). Moreover, survival had a slightly lower sensitivity of survivin (78.33%) than HAase (86.67%) for BC detection(68). These results indicate that HYAL-1 is useful for BC diagnosis. However, inconsistent find- ings have been reported in other studies. For example, Eissa et al. showed that UCA1 (91.5% and 96.5%) had a greater sensitivity and specificity than HYAL-1 (89.4 and 91.2%) for distinguishing BC patients from non- BC patients(80). These controversial results of the above studies might be due to different study countries and different total numbers of included people. Therefore, this network meta-analysis was important for providing a quantitative evaluation of the differences in the 58 in- cluded studies. Survivin is expressed in urine, and its expression is as- sociated with several adverse prognostic signs. Survivin can be reliably and quantitatively measured in the urine of BC patients, improving the sensitivity and specific- ity of urine cytology for BC diagnosis(68). A previous study showed that UC had lower sensitivity, accuracy, and negative predictive values than survivin for BC di- agnosis(70), which is consistent with our results. Moreo- ver, Chang et al. found that 73% of low-grade BC cases were diagnosed by positive survivin, while only 57.5% were diagnosed with positive UC(81). The survivin level is a more accurate test than the NMP22 test and the UC for the detection of lower grade and superficial BC (81), which further illustrates that survivin is suitable for BC diagnosis. HYAL-1 using real-time polymerase chain reaction (RT-PCR) is considered the best individual test, while enzyme-linked immunosorbent assay (ELISA) is the best test for survivin(68). Despite the lower sensitivity, specificity, and positive predictive value of survivin compared to HYAL-1, survivin detection has the ad- vantage of being a quantitative test measured through ELISA, which is lower cost and more easily performed than RT-PCR. In this study, the diagnostic results of urine biomark- ers (including BTA, FISH, UC, ImmunoCyt, NMP22, Sensitivity Specificity Positive Negative predictive Accuracy predictive Group Fixed Random Group Fixed Random Group Fixed Random Group Fixed Random Group Fixed Random HYAL-1 0.9775 0.9596 UC 0.9230 0.9431 HYAL-1 0.8882 0.8452 HYAL-1 0.9712 0.9694 HYAL-1 0.9930 0.9829 UCA1 0.8960 0.7546 FISH 0.7687 0.7867 UC 0.7763 0.8345 UCA1 0.9011 0.8496 Survivin 0.7678 0.8146 Survivin 0.7410 0.7170 HYAL-1 0.8543 0.7709 FISH 0.8247 0.7929 Survivin 0.7329 0.7136 UCA1 0.8624 0.6642 ImmunoCyt 0.5176 0.5429 ImmunoCyt 0.6575 0.5907 ImmunoCyt 0.7222 0.6872 ImmunoCyt 0.5630 0.5105 FISH 0.5032 0.5607 BTAtrak 0.2217 0.5019 NMP22 0.4001 0.4462 NMP22 0.4461 0.4107 FISH 0.5034 0.4800 ImmunoCyt 0.5868 0.4957 BTAstat 0.4666 0.3948 BTAstat 0.2559 0.3137 BTAstat 0.3413 0.3524 BTAtrak 0.2268 0.3612 BTAtrak 0.2430 0.3499 FISH 0.2716 0.3252 BTAtrak 0.4230 0.2703 BTAtrak 0.1931 0.2338 NMP22 0.2986 0.2945 BTAstat 0.1389 0.2332 NMP22 0.4078 0.3020 Survivin 0.1699 0.2168 Survivin 0.2236 0.2182 BTAstat 0.2754 0.2926 NMP22 0.1496 0.2145 UC 0.0002 0.0020 UCA1 0.0477 0.1615 UCA1 0.0846 0.1251 UC 0.0276 0.0286 UC 0.2554 0.1843 Table 6. Ranking results of network meta-analysis (P-score). UC, urine cytology; FISH, fluorescence in situ hybridization; NMP-22, nuclear matrix protein 22; UCA1: urothelial carcinoma associat- ed-1; HYAL-1, hyaluronidase 1; UC, urine cytology; ImmunoCyt, immunocyte; BTA, bladder cancer antigen. Urine biomarkers for diagnosing BC-Dong et al. Vol 18 No 6 November-December 2021 629 HYAL-1, UCA1, and survivin) for BC were analyzed for the first time using network meta-analysis, provid- ing certain clues and basis for further clinical diagnosis of BC. However, this study also had certain non-negligi- ble shortcomings. First, heterogeneity test showed that heterogeneity was statistically significant, which might be due to different study subjects (primary, recurrent, and metastatic) and different control groups (healthy and benign controls). As a potential confounding factor, heterogeneity might affect the results of the meta-anal- ysis. Second, sponsorship bias may exist in this study. Third, sensitivity analysis of the P-score was performed using the random effect and fixed effect models, while the ranking results were not completely consistent. Fur- thermore, the consistency test showed that the P-values of sensitivity and negative predictive value in BTA trak and NMP22 were < 0.05, which was inconsistent with the consistency test and proved unstable results. The in- consistency might be caused by insufficient literature and other biases (e.g., sponsor bias, selection bias, etc.). Finally, this study only focused on studies on subjects with suspected BC; thus, we will pay attention to this research direction of noninvasive detection tests for BC patients with hematuria in the future, and continue to conduct a meta-analysis. CONCLUSIONS In conclusion, HYAL-1 and survivin were found to be the two most suitable urine biomarkers for BC diagno- sis. However, more high-quality and rigorous studies are required to support our findings. CONFLICT OF INTEREST The authors declare that they have no competing finan- cial interests. APPENDIX https://journals.sbmu.ac.ir/urolj/index.php/uj/libraryFiles/downloadPublic/34 REFERENCES 1. Kaufman DS, Shipley WU, Feldman AS. Bladder cancer. Lancet. 2009;374:239-49. 2. Holz S, Albisinni S, Gilsoul J, et al. Risk factor assessment in high-risk, bacillus Calmetteâ“Guérin-treated, non-muscle- invasive bladder cancer. Res Rep Urol. 2017;9:195-202. 3. Freedman ND, Silverman DT, Hollenbeck AR, Arthur S, Abnet CC. Association between smoking and risk of bladder cancer among men and women. Jama. 2011;306:737-45. 4. Bertz S, Hartmann A, Knüchel-Clarke R, Gaisa NT. [Specific types of bladder cancer]. Der Pathologe. 2016;37:40. 5. Cheung G, Sahai A, Billia M, Dasgupta P, Khan MS. Recent advances in the diagnosis and treatment of bladder cancer. BMC Med. 2013;11:13-. 6. Cohen SM, Johansson SL. Epidemiology and etiology of bladder cancer. Urol Clin North Am. 2015;13:291-8. 7. Mcguire S. World Cancer Report 2014. Geneva, Switzerland: World Health Organization, International Agency for Research on Cancer, WHO Press, 2015. Adv Nutr. 2016;7:418. 8. Xylinas E, Kluth LA, Rieken M, Karakiewicz PI, Lotan Y, Shariat SF. Urine markers for detection and surveillance of bladder cancer. Urol Oncol. 2014;32:222-9. 9. Swellam M, Sadek M, Ahmady O, Khalifa A. Comparative Evaluation of the Nuclear Matrix Protein, Fibronectin, Urinary Bladder Cancer Antigen and Voided Urine Cytology in the Detection of Bladder Tumors - The Journal of Urology. J Urol. 2002;168:465-9. 10. Giannopoulos A, Manousakas T, Gounari A, Constantinides C, Choremi-Papadopoulou H, Dimopoulos C. Comparative evaluation of the diagnostic performance of the BTA stat test, NMP22 and urinary bladder cancer antigen for primary and recurrent bladder tumors. J Urol. 2001;166:470-5. 11. Reynolds JP, Voss JS, Kipp BR, et al. Comparison of urine cytology and fluorescence in situ hybridization in upper urothelial tract samples. Cancer Cytopathol. 2014;122:459- 67. 12. Chou R, Gore JL, Buckley D, et al. Urinary Biomarkers for Diagnosis of Bladder Cancer: A Systematic Review and Meta-analysis. Ann Intern Med. 2015;163:922. 13. Aiye G, Xiuhua W, Lan G, Juan S, Changyi S, Zhen W. Bladder tumour antigen (BTA stat) test compared to the urine cytology in the diagnosis of bladder cancer: A meta-analysis. Can Urol Assoc J. 2014;8:347-52. 14. Moher D, Shamseer L, Clarke M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4:1. 15. Whiting PF, Rutjes AWS, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155:529-36. 16. Polanin JR, Hennessy EA, Tanner-Smith EE. A Review of Meta-Analysis Packages in R. J Educ Behav Stat. 2016;42. 17. Zhang XH, Xiao C. Diagnostic Value of Nineteen Different Imaging Methods for Patients with Breast Cancer: a Network Meta-Analysis. Cell Physiol Biochem. 2018;46:2041-55. 18. Chao Z, Geng PL, Yi G, Zeng XT. Application of netmeta Package in R Language to Implement Network Meta-Analysis. Chinese Journal of Evidence-Based Medicine. 2014;14:625-30. 19. Higgins JP, Jackson D, Barrett JK, Lu G, Ades AE, White IR. Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Res Synth Methods. 2012;3:98–110. 20. Rücker G, Schwarzer G. Ranking treatments in frequentist network meta-analysis works without resampling methods. BMC Med Res Methodol. 2015;15:58. 21. Badawy T, El-Abd S, Zahra M, Eid M, Abdou S, El-Shazly S. Quantitative measurement of telomerase reverse transcriptase mRNA and chromosomal analysis of urine by M-FISH in the diagnosis and follow-up of bladder cancer. Mol Med Rep. 2008;1:325-33. Urine biomarkers for diagnosing BC-Dong et al. Urological Oncology 630 22. Melih B, Altug T, Ozer G, et al. Use of the nuclear matrix protein 22 Bladder Chek test? in the diagnosis of residual urothelial cancer before a second transurethral resection of bladder cancer. Int Urol Nephrol. 2015;47:473- 7. 23. Boman HS, Hedelin HS, Holmäng S. Four bladder tumor markers have a disappointingly low sensitivity for small size and low grade recurrence.: J. Urol 2002;167:80–83. J Urol. 2002;21:163-4. 24. Bubendorf L, ., Grilli B, ., Sauter G, ., Mihatsch MJ, Gasser TC, Dalquen P, . Multiprobe FISH for enhanced detection of bladder cancer in voided urine specimens and bladder washings. Am J Clin Pathol. 2001;116:79-86. 25. Chen A, Fu G, Xu Z, et al. Detection of Bladder Cancer Via Microfluidic Immunoassay and Single-Cell DNA Copy Number Alteration Analysis of Captured Urinary Exfoliated Tumor Cells. Cancer Res. 2018;78:4073-85. 26. Sun Y, He DL, Ma Q, et al. Comparison of seven screening methods in the diagnosis of bladder cancer. Chin Med J (Engl). 2006;119:1763-71. 27. Doğan C, Pelit ES, Yıldırım A, et al. The value of the NMP22 test for superficial bladder cancer diagnosis and follow-up. Turk J Urol. 2013;39:137. 28. Friedrich MG, Hellstern A, Hautmann SH, et al. Clinical use of Urinary Markers For The Detection And Prognosis Of Bladder Carcinoma: A Comparison Of Immunocytology With Monoclonal Antibodies Against Lewis X And 486p3/12 With The BTA STAT And NMP22 Tests. J Urol. 2002;168:470-4. 29. Giannopoulos A, ., Manousakas T, ., Mitropoulos D, ., et al. Comparative evaluation of the BTAstat test, NMP22, and voided urine cytology in the detection of primary and recurrent bladder tumors. Urology. 2000;55:871-5. 30. Ba?Os JL, Gutiérrez, Rodrigo MH, Rebollo, Juárez FM, Antolín, García B, Martín. NMP 22, BTA stat test and cytology in the diagnosis of bladder cancer: a comparative study. Urol Int. 2001;66:185-90. 31. Halling KC, King W, ., Sokolova IA, et al. A comparison of cytology and fluorescence in situ hybridization for the detection of urothelial carcinoma. J Urol. 2000;164:1768- 75. 32. Marcus H, Oliver P, J?Rg H, Erika S, Gerhard F, Arnulf S. Combinations of urine-based tumour markers in bladder cancer surveillance. Scand J Urol Nephrol. 2009;43:461-6. 33. Huang JW, Mu JG, Li YW, et al. The utility of fluorescence in situ hybridization for diagnosis and surveillance of bladder urothelial carcinoma. Urol J. 2014;11:1974-9. 34. Gawad IA, Abd El, Moussa HS, Nasr MI, et al. Comparative study of NMP-22, telomerase, and BTA in the detection of bladder cancer. J Egypt Natl Canc Inst. 2005;17:193. 35. Ishiwata S, Takahashi S, Homma Y, et al. Noninvasive detection and prediction of bladder cancer by fluorescence in situ hybridization analysis of exfoliated urothelial cells in voided urine. Urology. 2001;57:811-5. 36. Jalil H, Ali Reza G, Mohammad Mohsen M, et al. Detection of recurrent bladder cancer: NMP22 test or urine cytology? Urol J. 2012;9:367-72. 37. Kojima T, Nishiyama H, Ozono S, et al. Clinical evaluation of two consecutive UroVysion fluorescence in situ hybridization tests to detect intravesical recurrence of bladder cancer: a prospective blinded comparative study in Japan. Int J Clin Oncol. 20181-8. 38. Laudadio J, Keane TEReeves HM, Savage SJ, Hoda RS, Lage JM, Wolff DJ. Fluorescence in situ hybridization for detecting transitional cell carcinoma: implications for clinical practice. Urol Oncol. 2005;24:270-1. 39. Lavery HJ, Zaharieva B, Mcfaddin A, Heerema N, Pohar KS. A prospective comparison of UroVysion FISH and urine cytology in bladder cancer detection. Bmc Cancer. 2017;17:247. 40. Leyh H, Marberger M, ., Conort P, ., Sternberg C, ., Pansadoro V, ., Pagano F, ., et al. Comparison of the BTA stat test with voided urine cytology and bladder wash cytology in the diagnosis and monitoring of bladder cancer. Eur Urol. 1999;35:52-6. 41. Hong-Xia L, Ming-Rong W, Huan Z, Jian C, Chang-Ling L, Qin-Jing P. Comparison of fluorescence in situ hybridization, NMP22 bladderchek, and urinary liquid-based cytology in the detection of bladder urothelial carcinoma. Diagn Cytopathol. 2013;41:852-7. 42. March-Villalba JA, Panach-Navarrete J, Herrero-Cervera MJ, Aliño-Pellicer S, Martínez-Jabaloyas JM. hTERT mRNA expression in urine as a useful diagnostic tool in bladder cancer. Comparison with cytology and NMP22 BladderCheck Test®. Actas Urol Esp. 2018;42:524-30. 43. Mercedes MA, Lourdes M, María José R, et al. Utility of a multiprobe fluorescence in situ hybridization assay in the detection of superficial urothelial bladder cancer. Cancer Genet Cytogenet. 2007;173:131-5. 44. May M, Hakenberg OW, Gunia S, et al. Comparative Diagnostic Value of Urine Cytology, UBC-ELISA, and Fluorescence In Situ Hybridization for Detection of Transitional Cell Carcinoma of Urinary Bladder in Routine Clinical Practice. Urology. 2007;70:449-53. 45. Meiers I, Singh H, Hossain D, et al. Improved filter method for urine sediment detection of urothelial carcinoma by fluorescence in situ hybridization. Arch Pathol Lab Med. 2007;131:1574. 46. Moonen PMJ, Merkx GFM, Peelen P, Karthaus HFM, Smeets DFCM, Witjes JA. UroVysion Compared with Cytology and Quantitative Cytology in the Surveillance of Non–Muscle-Invasive Bladder Cancer. Eur Urol. 2007;51:1275-80. Urine biomarkers for diagnosing BC-Dong et al. Vol 18 No 6 November-December 2021 631 47. O'Sullivan P, Sharples K, Dalphin M, et al. A Multigene Urine Test for the Detection and Stratification of Bladder Cancer in Patients Presenting with Hematuria. J Urol. 2012;188:741-7. 48. Pesch B, Taeger D, Johnen G, et al. Screening for bladder cancer with urinary tumor markers in chemical workers with exposure to aromatic amines. Int Arch Occup Environ Health. 2014;87:715-24. 49. Placera J, Salido M, Solé F, Gelabert-Mas A. Clinical Utility of a Multiprobe FISH Assay in Voided Urine Specimens for the Detection of Bladder Cancer and its Recurrences, Compared with Urinary Cytology. Eur Urol. 2002;42:547-52. 50. PODE, SHAPIRO, WALD, NATIV, LAUFER. Noninvasive detection of bladder cancer with the BTA stat test. Commentary. J Urol. 1999;161:443-6. 51. Raitanen MP, Marttila T, ., Nurmi M, ., et al. Human complement factor H related protein test for monitoring bladder cancer. J Urol. 2001;165:374-7. 52. Ramakumar S, Bhuiyan J, Besse JA, et al. COMPARISON OF SCREENING METHODS IN THE DETECTION OF BLADDER CANCER. J Urol. 1999;161:388- 94. 53. Rhijn BW, Van, Lurkin I, ., Kirkels WJ, Kwast TH, Van Der, Zwarthoff EC. Microsatellite analysis--DNA test in urine competes with cystoscopy in follow-up of superficial bladder carcinoma: a phase II trial. Cancer. 2015;92:768-75. 54. Saad A, Hanbury DCMcNicholas TA, Boustead GB, Morgan S, Woodman AC. A study comparing various noninvasive methods of detecting bladder cancer in urine. BJU Int. 2015;89:369-73. 55. Sarosdy MF SP, Bokinsky G, Kahn P, Chao R, Yore L, et al. Clinical Evaluation of a Multi-target Fluorescent in Situ Hybridization Assay for Detection of Bladder Cancer. J Urol. 2002;168:1950-4. 56. Serretta V, ., Pomara G, ., Rizzo I, ., Esposito E, . Urinary BTA-stat, BTA-trak and NMP22 in surveillance after TUR of recurrent superficial transitional cell carcinoma of the bladder. Eur Urol. 2000;38:419-25. 57. Sharma S, ., Zippe CD, Pandrangi L, ., Nelson D, ., Agarwal A, . Exclusion criteria enhance the specificity and positive predictive value of NMP22 and BTA stat. J Urol. 2013;162:53-7. 58. Ruchi S, Vinod Kumar A, Seema A, Arati B, Navjeevan S, Vivek A. Cytokeratin-20 immunocytochemistry in voided urine cytology and its comparison with nuclear matrix protein-22 and urine cytology in the detection of urothelial carcinoma. Diagn Cytopathol. 2012;40:755-9. 59. Sullivan PS, Farzad N, Hope S, et al. Comparison of ImmunoCyt, UroVysion, and urine cytology in detection of recurrent urothelial carcinoma: a "split-sample" study. Cancer Cytopathol. 2010;117:167-73. 60. Toma MI, Friedrich MG, Hautmann SH, et al. Comparison of the ImmunoCyt test and urinary cytology with other urine tests in the detection and surveillance of bladder cancer. World J Urol. 2004;22:145-9. 61. Tsui KH CS, Wang TM, Juang HH, Chen CL, Sun GH, et al. Comparisons of voided urine cytology, nuclear matrix protein-22 and bladder tumor associated antigen tests for bladder cancer of geriatric male patients in Taiwan, China. Asian J Androl 2007;9:711-5. 62. V P UW, R DV, al e. A comparison of urinary nuclear matrix protein-22 and bladder tumour antigen tests with voided urinary cytology in detecting and following bladder cancer: the prognostic value of false-positive results. BJU Int. 2001;88:692-701. 63. Varella-Garcia M, Akduman B, Sunpaweravong P, Maria MVD, Crawford ED. The UroVysion fluorescence in situ hybridization assay is an effective tool for monitoring recurrence of bladder cancer. Urol Oncol. 2004;22:16-9. 64. Ravindra V, Nordberg ML, Runhua S, Herrera GA, Turbat-Herrera EA. Evaluation of fluorescence in situ hybridization as an ancillary tool to urine cytology in diagnosing urothelial carcinoma. Diagn Cytopathol. 2010;28:301-7. 65. Wiener HG, Mian C, ., Haitel A, ., Pycha A, ., Schatzl G, ., Marberger M, . Can urine bound diagnostic tests replace cystoscopy in the management of bladder cancer? J Urol. 1998;159:1876-80. 66. Yafi FA, Brimo F, Steinberg J, Aprikian AG, Tanguay S, Kassouf W. Prospective analysis of sensitivity and specificity of urinary cytology and other urinary biomarkers for bladder cancer. Urol Oncol. 2015;33:66.e25- 66.e31. 67. Eissa S, Swellam M, Shehata H, El-Khouly IM, El-Zayat T, El-Ahmady O. Expression of HYAL1 and survivin RNA as diagnostic molecular markers for bladder cancer. J Urol. 2010;183:493-8. 68. Eissa S, Badr S, Barakat M, Zaghloul AS, Mohanad M. The diagnostic efficacy of urinary survivin and hyaluronidase mRNA as urine markers in patients with bladder cancer. Clin Lab. 2013;59:893-900. 69. Eissa S, Matboli M, Essawy NO, Shehta M, Kotb YM. Rapid detection of urinary long non-coding RNA urothelial carcinoma associated one using a PCR-free nanoparticle- based assay. Biomarkers. 2015;20:212-7. 70. Abd El-Hakim TF, El-Shafie MK, Abdou AG, Azmy RM, El-Naidany SS, Badr El-Din MO. Value of urinary survivin as a diagnostic marker in bladder cancer. Anal Quant Cytopathol Histpathol. 2014;36:121-7. 71. Ganas V, Kalaitzis C, Sountoulides P, Giannakopoulos S, Touloupidis S. Predictive values of urinary bladder tumor markers survivin and soluble-Fas comparison with cystoscopy and bladder tumor antigen. Minerva Urol Nefrol. 2012;64:279-85. 72. Milowich D, Le Mercier M, De Neve N, et al. Diagnostic value of the UCA1 test for Urine biomarkers for diagnosing BC-Dong et al. Urine biomarkers for diagnosing BC-Dong et al. bladder cancer detection: a clinical study. Springerplus. 2015;4:349. 73. Montalbo R, Izquierdo L, Ingelmo-Torres M, et al. Urine cytology suspicious for urothelial carcinoma: Prospective follow-up of cases using cytology and urine biomarker-based ancillary techniques. Cancer Cytopathol. 2020;128:460-9. 74. Muhammad AS, Mungadi IA, Darlington NN, Kalayi GD. Effectiveness of bladder tumor antigen quantitative test in the diagnosis of bladder carcinoma in a schistosoma endemic area. Urol Ann. 2019;11:143-8. 75. Muhammad AS, Mungadi IA, Ndodu ED, Kalayi GD. Performance of urinary survivin as a non-invasive molecular marker of bladdercarcinoma in a schistosomiasis endemic area. Ghana Med J. 2018;52:74-8. 76. Pu XY, Wang ZP, Chen YR, Wang XH, Wu YL, Wang HP. The value of combined use of survivin, cytokeratin 20 and mucin 7 mRNA for bladder cancer detection in voided urine. J Cancer Res Clin Oncol. 2008;134:659-65. 77. Sajid MT, Zafar MR, Ahmad H, Ullah S, Mirza ZI, Shahzad K. Diagnostic accuracy of NMP 22 and urine cytology for detection of transitional cell carcinoma urinary bladder taking cystoscopy as gold standard. Pak J Med Sci. 2020;36:705-10. 78. Srivastava AK, Singh PK, Srivastava K, et al. Diagnostic role of survivin in urinary bladder cancer. Asian Pac J Cancer Prev. 2013;14:81- 5. 79. Kramer MW, Escudero DO, Lokeshwar SD, et al. Association of hyaluronic acid family members (HAS1, HAS2, and HYAL-1) with bladder cancer diagnosis and prognosis. Cancer. 2011;117:1197-209. 80. Eissa S, Matboli M, Essawy NO, Kotb YM. Integrative functional genetic-epigenetic approach for selecting genes as urine biomarkers for bladder cancer diagnosis. Tumour Biol. 2015;36:9545-52. 81. Chang SH, Kim YH, Kim ME. Urinary Survivin Test Compared to the Nuclear Matrix Protein (NMP)-22 Test and Urine Cytology for the Diagnosis of Bladder Cancer. Korean J Urol. 2006;47:1041-5. Urological Oncology 632