Next Article in Journal
Parenting Stress, Maternal Self-Efficacy and Confidence in Caretaking in a Sample of Mothers with Newborns (0–1 Month)
Next Article in Special Issue
Verbal Instruction for Pelvic Floor Muscle Contraction among Healthy Young Males
Previous Article in Journal
The Relationship between School Infrastructure and School Nutrition Program Participation and Policies in New York City
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 19
Issue 15
10.3390/ijerph19159648
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

The Role of Novel Bladder Cancer Diagnostic and Surveillance Biomarkers—What Should a Urologist Really Know?

by
Rafaela Malinaric
1,2,*,
Guglielmo Mantica
1,
Lorenzo Lo Monaco
1,2,
Federico Mariano
1,2,
Rosario Leonardi
3,
Alchiede Simonato
4,
André Van der Merwe
5 and
Carlo Terrone
1,2
1
Department of Urology, IRCCS Policlinic Hospital San Martino, 16132 Genoa, Italy
2
Dipartimento di Scienze Chirurgiche e Diagnostiche Integrate (DISC), University of Genoa, 16132 Genoa, Italy
3
Department of Urology, Casa di Cura Musumeci GECAS, 95030 Gravina di Catania, Italy
4
Department of Surgical, Oncological and Stomatological Sciences, University of Palermo, 90133 Palermo, Italy
5
Department of Urology, Tygerberg Academic Hospital, Stellenbosch University, Cape Town 7600, South Africa
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(15), 9648; https://doi.org/10.3390/ijerph19159648
Submission received: 9 July 2022 / Revised: 29 July 2022 / Accepted: 31 July 2022 / Published: 5 August 2022
(This article belongs to the Special Issue Advances in Male Reproductive Health and Urology)
Browse Figure
Review Reports Versions Notes

Abstract

:
The aim of this review is to analyze and describe the current landscape of bladder cancer diagnostic and surveillance biomarkers. We researched the literature from 2016 to November 2021 to find the most promising new molecules and divided them into seven different subgroups based on their function and location in the cell. Although cystoscopy and cytology are still the gold standard for diagnosis and surveillance when it comes to bladder cancer (BCa), their cost is quite a burden for national health systems worldwide. Currently, the research is focused on finding a biomarker that has high negative predictive value (NPV) and can exclude with a certainty the presence of the tumor, considering missing it could be disastrous for the patient. Every subgroup has its own advantages and disadvantages; for example, protein biomarkers cost less than genomic ones, but on the other hand, they seem to be less precise. We tried to simplify this complicated topic as much as possible in order to make it comprehensible to doctors and urologists that are not as familiar with it, as well as encourage them to actively participate in ongoing research.

1. Introduction

The term ‘biomarker’ has been used in scientific language since the 1970s and the most accepted definition of it is ‘a defined characteristic that is measured as an indicator of normal biological processes, pathogenic processes or responses to an exposure or intervention’. Biomarkers can be found at molecular, cellular and tissue levels [1].
The advantage of biomarkers is their simplicity in the method of clinical endpoint measurement, and their reproducibility, along with repeated, immediate analysis and cost-effectiveness when compared to other diagnostic and staging tools [1,2].
When both sexes are considered, bladder cancer (BCa) is currently one of the most common cancers worldwide, sitting at seventh place. Around 75% of patients present with a disease confined to the mucosa or submucosa [3], but they often recur [4,5]. At this moment, urinary cytology and cystoscopy represent the standard of care in both diagnosis and surveillance [6,7]. Cytology, although having a great sensitivity for high-grade tumors (84%), is not performing as well for low-grade tumors (16%) [8]. Furthermore, the interpretation of it is user-dependent and can be easily tempered [9,10]. For example, reactive urothelial atypia during cystitis is difficult to recognize when compared with recurrent CIS [11]. Moreover, there is a high proportion of cytology samples that are categorized as “atypical” [12]. Cystoscopy, in contrast, is sensitive for papillary lesions but not as much for flat ones. It also bares certain risks, especially in elderly and fragile patients [6]. Finally, in addition to the guidelines, surveys have shown that patients prefer undergoing cystoscopy, accepting its risks, than having uncertainties regarding their oncological status by taking noninvasive blood or urine tests [13,14]. Actually, some patients undergo cystoscopies more frequently than recommended, associating this kind of surveillance with increased medical costs [15,16,17,18].
What we consider an effective assessment that could greatly benefit current practice, is a low-cost, rapid and effective test with high sensitivity and negative predictive value. By ruling out the presence of bladder cancer, it would help patients avoid the cystoscopy [19,20,21,22,23].
Several urinary tests have been developed during recent years, but none of the novel biomarkers met the criteria to be proposed as a routine practice in diagnosis and follow-up. The main reasons are lower specificity when compared to cytology, clinical context dependency when applicated, and complicated laboratory methods, although there are a few that have yielded a high negative predictive value (NPV) [3].
The aim of this review is to summarize current evidence and provide to urologists an overview of this sometimes quite complicated landscape.

2. Materials and Methods

2.1. Literature Research Strategy

We conducted systemic research on Pubmed/Medline and Web of Science using the following Medical Subject Headings (MeSH) terms: ((“Biomarkers, Tumor” [Mesh]) AND (“Diagnosis” [Mesh]) OR (“Early Detection of Cancer” [Mesh]) AND (“Urinary Bladder Neoplasms” [Mesh])) from 2016 to November 2021, wanting to evaluate the newest biomarker studies and proposals.
The updated Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were followed for this systematic review [24]. The search was performed independently by two researchers (R.M. and G.M.), and any disagreement resolved by a third independent researcher (L.L.M.). The initial screening was done based on titles and abstracts.

2.2. Inclusion and Exclusion Criteria

All papers published after 2016 concerning studies conducted on diagnostic and surveillance biomarkers for bladder cancer were considered for the review.
We included full text articles written in the English language, with a cohort of at least 10 patients, aged 18 and older. After reviewing each manuscript, we also searched its references for any relevant manuscripts missed. We excluded the systematic/narrative reviews, meta-analyses, nonrelevant articles, editorials or letters to the editor, case report articles, and studies on the accuracy of biomarkers in assessing prognosis, monitoring treatment response or therapy guided.

2.3. Data Extraction Design

Biomarkers were classified into 7 different categories: altered proteins, genomic mutations, circulating free nucleic acids, epigenetic changes and transcriptomic alterations, metabolic-induced changes, combination of more than one category and improving conventional cytology.
Two investigators (R.M. and G.M.) extracted details about study design, biomarkers studied, evaluated tests, population studied and its characteristics, bladder cancer grade and stage and results. A third investigator (L.L.M.) verified extractions for accuracy and was consulted for discrepancies, which were resolved through consensus.
We constructed Table 1 comparing sensitivities and specificities of the biomarkers from the published articles (data extracted from the results section of each manuscript).

2.4. Statistical Analysis

Data were entered into a Microsoft Excel (Version 2019) database. Descriptive statistics were calculated for all demographic, clinical and follow-up variables and reported as median or as a proportion with percentage.

3. Results

By searching the above-mentioned MeSH terms, we identified 778 articles in Pubmed/MEDLINE and 49 articles in the Web of Science database; 10 articles were present in both databases, therefore, the total number of abstracts reviewed was 817.
Subsequently, 691 articles were excluded after full text review because they did not meet inclusion criteria, and 36 articles were added from additional sources (references of the reviewed manuscripts). Therefore, 118 articles were eligible for the final analysis.
The PRISMA flowchart is shown in Figure 1.
This review was registered in The International Prospective Register of Systematic Reviews (PROSPERO) database (the protocol No. CRD42021270777).

3.1. Altered Proteins

Some authors believe that the detection of altered proteins is the most appropriate due to minimal sample processing and lower costs, without requiring molecular target amplification. Usually, altered proteins are detected in assays and by spectrometry [25,26].
For quite some time, scientists were oriented towards investigating filaments of cytoskeleton, the cytokeratins. Their expression is dependent on the cellular differentiation and its degree, so they represent a good biomarker for distinguishing carcinomas [27]. Four types of cytokeratins were tested as potential urinary biomarkers: 8, 17, 18 and 20. The only commercialized test using cytokeratins (8 and 18) is UBC rapid test. It provides diagnosis after 10 min using a photometric reader. Although very fast, practical and cheap, the UBC test’s sensitivity (30–87%) and specificity (63–91%) [28,29,30,31,32] are quite limited. It was, however, proved to be a useful in adjunct to the cytology with immunostaining [33,34].
Another structural protein proposed as a biomarker is nuclear matrix protein 22. During the cell-division cycle, NMP22 is released from the apoptotic cells and its presence increases significantly in BCa patients [35]. NMP22 was approved as a diagnostic biomarker by the Food And Drug Administration (FDA) (Bladdercheck), but this biomarker is not recommended for routine use because of its variable sensitivity (37.9–88.5%), depending mostly on the grade and stage, and specificity (65.2–96.9%) in bladder cancer detection [36].
Onsidering cellular signaling proteins such as Orsomucoid-1 (acute-phase protein [37]), Osteonectin, BM40 or Secreted Protein Acidic and Rich in Cystein (SPARC—Extracellular matrix protein that promotes cellular adhesion [38]), Apurinic/apyrimidinic endonuclease 1/redox factor-1 (DNA repair and activation of transcriptional factors [39]), soluble Fas (programmed cellular death via Fas/APO-1/CD95 [40]), Aurora kinase A (AURKA—Assembly of mitotic spindle and facilitation of the mitosis [41]), Serum Irisin, (activator of gene promoters for the browning of the white adipose tissue [42]), Amplified in breast cancer 1 (AIB1—Transcriptional activity [43]) in combination with EIF5A2 (protein synthesis and in translation elongation [44]), when employed as a diagnostic biomarkers yielded variable sensitivities and specificities, ranging from 39% to 93% and 70% to 94%, respectively [45,46]. Although outperforming urine cytology when it comes to sensitivity, some were largely influenced by the presence of hematuria, or did not achieve satisfactory NPV to be considered for the everyday practice use.
LIM and SH3 protein 1 (LASP1), adhesion proteins, were also tested, but subsequently excluded as diagnostic biomarkers because of false positives high rates [47].
Protein biomarkers are much more likely to be diagnostic if used in pairs or in assays. In fact, tissue-like isoforms (unphosphorylated and Ser253- and Ser258-phosphorylated, involved in blood clotting via extrinsic pathway and angiogenesis) were shown to be quite specific for the BCa cohort. With an optimum cut-off established, this assay yielded high specificity and NPV, but it needs further investigatory trials [48]. On the other hand, a multiplex immunoassay containing 10 protein biomarkers (angiogenin, apolipoprotein E, alpha-1 antitrypsin, carbonic anhydrase 9, interleukin 8, matrix metallopeptidase 9 and 10, syndecan 1, plasminogen activator inhibitor 1 and vascular endothelial growth factor A), commercially launched as Oncuria, achieved an overall sensitivity of 87% and specificity of 92%, but when corrected for three demographic factors (sex, age, race) the overall sensitivity and specificity reached 93%, with positive predictive value (PPV) of 65% and NPV of 99% [49,50,51].

3.2. Genomic Mutations

Cancer is a result of somatic mutations in which clonal lineage gains mutations that allow cell survival in a hostile environment [52]. By using The Cancer Genome Atlas (TCGA) we can now identify and correlate different genes that are most frequently mutated to the specific types of cancer [53].
One of the most important characteristics of the cancerous cells is to proliferate indefinitely. Deoxyribonucleic acid (DNA) polymerase limits normal cell proliferation by shortening chromosomes, but if telomeres are added, the cell continues to duplicate. Telomeres can be shortened by telomerase, an enzyme that is regulated by TERT (Telomerase reverse transcriptase) promoter. Cancer cells do not downregulate telomerase [54].
Meanwhile, Fibroblasts Growth Factor Receptors (FGFR) are transmembrane receptors kinases that, when activated, promote cell proliferation, differentiation, growth, migration and apoptosis [55]. FGFR3 mutation is usually associated with shorter time to recurrence in low-grade disease [56].
One of the novel urine-based assays that detects mutations in three genes (TERT, FGFR3 and KRAS), FDA-approved Uromonitor, was investigated in two major studies. With sensitivity that varied from 79% to 98%, specificity 62–90% and NPV 86–99%, the test showed potential as an alternative to the current follow-up methods, to be confirmed in larger cohorts of BCa patients [57,58].
Multicentric and prospective study done by Beukers et al. evaluated the combination of TERT, FGFR3 and OTX1 mutations panel. During surveillance 57% of recurrences were identified, with specificity value of 59%. The majority of tumors missed were low-grade tumors (45–49%), whilst T1, T2-4, high-grade G3 tumors were identified in 88%, 77% and 83% of patients during follow-up. FGFR3 mutation can be combined with other common mutations, such as Cyclin D3, or in an assay containing TP53, PIK3CA, ARID1A, STAG2 and KTM2D, yielding sensitivity, specificity and NPV of 73–95%, 85–90% and 0.82, respectively [59,60,61,62].
TERT and PLEKHS1 promoter mutations were demonstrated to be more sensitive than cytology alone, especially in low-grade tumors. Overall sensitivity, specificity and NPV varied, reaching values of 91–96.7%, 96–100% and 87.5% [62].
ASPM, or the abnormal spindle-like microcephaly associated gene, involved in mitotic spindle formation, overexpresses in neoplastic tissues, while TEF, or thyrotrophic embryonic factor, is involved in transcription by binding DNA, hence controlling cell cycle [63]. Saleh et al. analyzed the expression of these two genes in BCa and found ASPM upregulation and TEF downregulation in cancerous tissue when compared to the healthy one. ASPM gene showed sensitivity of 95.56% and specificity of 86.67%, whilst TEF 91.11% and 71.11%. Furthermore, these two genes have the ability to predict occurrence of metastatic lesions, being significantly over or under expressed in high-grade tumors, and smokers [64].
Further, minichromosome maintenance protein 5 (MCM5) is an essential protein when it comes to the initiation of DNA replication process. It is usually present only on the surface of actively dividing cells [65]. ADXBLADDER MCM5 is an ELISA test that utilizes detection of MCM5 via MCM5 antibodies in urine [65]. This test was evaluated in a multicentric, blinded, prospective study where it outperformed cytology’s sensitivity. It yielded sensitivity of 63–79%, specificity of 65–88% and a negative predictive value of 87–99%. In addition, ADXBLADDER does not require an interpretation of an expert pathologist and the result is available in 3h. However, in some studies, pTa low-grade cases were regarded as negative for BCa, increasing, therefore, NPV. Nevertheless, according to these results, a negative test could indicate the absence of the recurrence [65,66,67,68,69,70].
Lastly, when repeated alterations in the genome occur, they can cause microsatellite instability. Consequently, frameshift mutations occur and stimulate tumor growth [71]. Loss of heterozygosity (LOH) is another molecular change that affects gene loci, and it occurs when one of the microsatellite alleles is present in the normal DNA but not in the paired tumor DNA. When profiling for microinstability an Egyptian study found a concordance between urine and tumor tissue samples regarding D16S476, D9S171, FGA, and ACTBP2 alterations (MSI and/or LOH), therefore, they evaluated it as a potential biomarker [72].

3.3. Circulating Free Nucleic Acids

Nucleic acids are present in bodily liquids independently of cells. Cell-free nucleic acids (CfNAs) can contain nuclear DNA, mitochondrial DNA, and all types of RNA. CfDNA can be found free, vesicle-bound or in DNA-macromolecular complexes, whilst RNAs in extracellular vesical membranes (EVM) or form ribonucleoprotein complexes. In addition, circulating DNA-protein complexes are double-strand DNA (dsDNA) fragments wrapped around the histone protein complex, virtosomes and are released during apoptosis. Mitochondrial DNA, in contrast, is secreted exclusively in EVMs. Various types of RNAs are divided in two major groups: coding (for proteins) and noncoding [73].
Tumor cell-free DNA can be found in urine of patients, so its quantitative analysis has been explored as a possible biomarker for BCa detection. Brisuda et al. found that urine-derived cfDNA was not significantly more advantageous than urine cytology, having sensitivity and specificity of 20.7 and 91.2%, respectively, in low-grade and 59.5 and 91.2%, respectively, in high-grade tumors [74]. On the other hand, serum and urine exosomes performed quite well, showing specificity of 100% in serum and 83.3% in urine, whilst sensitivity of 92.6% in urine and 82.4% in serum. The correlation between exosomes levels and invasiveness of bladder cancer was also noted [75].
CxBladder is an FDA-approved, multiplex and multi-assay urinary test that quantifies messenger RNA (mRNA). This test can detect cancer in the patients’ urine and estimate the probability of its presence by extracting and quantifying these five mRNAs: MDK (migration and angiogenesis of the cancerous cells), HOX13 (affects cell differentiation), CDC2 (completion of cell cycle), IGFBP5 (inhibits the apoptosis) and CXCR2 (crucial for inflammatory response). Based on the clinicians’ requirements Cxbladder offers three types of urinary tests: Triage (high NPV to rule out the presence of cancer in patients with hematuria), Detect (in adjunct to cystoscopy and other diagnostic procedures aiming to detect recurrent bladder cancer with higher accuracy) and Monitor (presents high sensitivity and it should be able to replace more invasive surveillance procedures) [75]. With a sensitivity of 91%, specificity of 61% and NPV of 98% for detecting high-grade recurrence, it was found that CxbM could be useful as a rule-out test to identify the patients who are low-risk and do not require cystoscopy. In total, 77.8% of patients were managed in a safe and feasible manner like that, repeating cystoscopy every two years. Moreover, 35% of unnecessary cystoscopies were spared [76,77,78,79].
Alternatively, Xpert Bladder is a novel, extremely practical and low-cost urinary test for BCa detection. By quantifying five target mRNAs (CRH, IGF2, UPK1B, ANXA10 and ABL1), it automatically prepares the sample, amplifies and targets the desired sequences to give a positive or a negative response. Overall sensitivity of the test was 63–80%, specificity 73–81% and NPV 92%. On the other hand, when stratified by grade, it achieved NPV of 99% for high-grade recurrences, so this could imply less cystoscopies in patients harboring this kind of tumors [66,80,81,82,83,84,85,86,87,88,89].
Other researched mRNAs present in higher levels of BCa patients’ urine are Urine Ubiquitin Conjugating Enzyme E2 C (UBE2C—A member of the anaphase promoting cyclosome), QGAP3 (cell proliferation, cytoskeleton remodeling, cellular adhesion and signaling), and N-Myc downstream-regulated gene 2 (cell proliferation and apoptosis promotion). UBE2C mRNA was found to be particularly useful in discriminating the origin of hematuria [90], QGAP3 in differential diagnosis in gross and microhematuria, whilst N-Myc mRNA levels were highly correlated with grade and stage of the disease. Overall, singularly, these mRNAs achieved sensitivity 80–96.2%, and specificity that varied from 60.2 to 90.7%) [90,91,92].

3.4. Epigenetic Changes and Transcriptomic Alterations

Micro RNAs (miRNAs) are small non-coding RNAs transcribed from DNA sequences. They usually interact with untranslated regions (regions that do not carry information from DNA to the ribosomes, the site of the protein synthesis). However, recent research suggests that miRNAs might have hormone-like activities by influencing protein levels of the target mRNAs without affecting the gene sequences [93].
Usually, epigenetic biomarkers are not used singularly, but in assay, as multiple miRNAs combined in a panel showed greater diagnostic performance [94]. Study done by Urquidi et al. determined 25 target miRNAs by using multivariate model and concluded that the diagnostic ability of each biomarker increases by shrinking the regression coefficients of the parameters [95]. They can be targeted by using next generation sequencing (NGS), although, with this technology some discrepancies were observed when compared with real-time polymerase chain reaction (PCR). Considering these findings, it cannot be stated with certainty which method is best for detecting urinary miRNAs [96].
A lot of miRNAs subtypes have been studied recent years, singularly and in assays, and their altered concentrations in the patients’ urine, with quite high sensitivity and specificity [94,97,98,99,100,101,102]. Interestingly, we can combine them with other diagnostic tools, such as suprapubic ultrasound. In fact, when miRNA-192 was combined with 2D bladder ultrasound the sensitivity and specificity were 93.2% and 96.7%, respectively, not significantly different from that of cystoscopy [100]. Moreover, in a study that tested a diagnostic assay containing 12 miRNAs, it was hypothesized that it could lower the cystoscopy rates by 30% [103], while a panel containing only 7 miRNAs significantly outperformed cytology in low-grade tumors (sensitivity = 80.88% and specificity = 91.67%) [98]. Juracek et al. concluded that only one pair of miRNAs allows discrimination between grades and stages and was significantly decreased in patients in postoperative period if disease-free [99]. MiRNAs can be retrieved from urine, but also from the patients’ serum. The combination of seven serum miRNAs resulted to be quite characteristic of BCa, yielding overall high sensitivity (95–98%) and specificity (87–91%), and, with two specific miRNAs, miRNA-6087 and miRNA6831-5p, showed remarkable diagnostic power for detecting BCa and distinguishing it from other solid tumors [104]. The three miRNAs serum panel also showed quite a promise, although not as such as a seven-component panel [102].
On the other hand, studies have confirmed the importance of DNA methylation regulation in cancer transformation, preservation, and progression. If DNA methylation becomes dysregulated, it influences not only the integrity of the genome, and thus dysregulation of gene expression, but also the immune modulation-response [105].
Urine-based DNA methylation test containing a panel of 15 (unpublished) genes, commercially known as Bladder EpiCheck®, was the first test used for monitoring bladder cancer patients. Based on the methylation levels present in the urine, the test gives a numeric value between 0 to 100 (EpiScore), considering it positive for BCa if Episcore results over 60. The test yielded an overall sensitivity of 57–85% (91.7% when low-grade tumors were excluded) and a specificity of 80–88.0%. After excluding low-grade recurrent tumors, sensitivity and NPV increased, with NPV up to 99% [106,107,108,109,110,111]. One study compared Bladder EpiCheck and Bladder Xpert Monitor performance (measures mRNA in the patients’ urine—ABL1, CRH, IGF2, UPK1B and ANXA10) with those of cytology and found that these new tests outperform cytology when it comes to sensitivity, but do not reach its excellence when it comes to specificity. NPV was very similar for the three tests, but when combined, two novel tests detected 79.35% of the tumors: 70.37% low grade and 92.11% high grade [66].
Combining genes such as TWIST1, OTX1 (Homebox 1 codes for a transcriptional protein), and TERT in combination with FGFR3 [60,112,113,114,115,116] yielded the most success when evaluating abnormal methylation of tumor suppressor genes [117]. By testing them, a high-throughput microdroplet-based PCR amplification system (UroMark) [118,119] showed sensitivity of 87–98%, specificity 96% and NPV of 97% [119].
RNA has an important role of being intermediary between DNA and encoded proteins, and although only 1.5% of human DNA encodes for protein genes, 90% of the genome is actively transcribed. This process is extremely complex and consists of various types of RNA. For example, long non-coding RNAs (lncRNAs) are involved in majority of cellular cycle processes and regulate diverse functions such as controlling apoptosis, cell death and cell growth [120].
Firstly, Sin et al. applied RNA sequencing tool for discovering bladder-cancer specific, 3-component mRNA panel, ergo urinary concentration of ROBO1 (promoter of tumor angiogenesis, BCa specific), WNT5A (regulation of cell polarity and migration, BCa specific) and CDC42BPB (the reference gene) mutational load. This assay was evaluated in an independent cohort and later compared to conventional cytology. It yielded overall sensitivity and specificity of 83% and 89%, respectively, and was more sensitive than CxBladder in low-grade tumors [121].
Alternatively, lncRNAs can be identified in the patients’ urine and serum, and they too preferably in panels. LncRNAs serum panels, developed and tested, achieved AUC of 0.857 and 0.826, significantly higher than that of urine cytology. Additionally, the researchers noticed that non-muscle-invasive BCa patients with high UBC1 lncRNA expression had significantly lower recurrence-free survival. Ultimately, overall sensitivity was 80%, whilst specificity ranged from 73% to 75% [122,123].
Other panels, detecting urinary lncRNAs, such as UCA1-201, UCA1-203, MALAT1 or HOTAIR lncRNAs, yielded greater sensitivity and specificity (43.5–100% and 72–100%, respectively) than those targeting serum lncRNAs, but those too need further research and trials before taken in consideration for any further clinical application [122,123,124,125,126,127,128,129].
Transcriptomic alterations are quite powerful diagnostic tool as well, so much that the expression of only two genes, IGF2 and MAGEA3, yielded almost the same sensitivity and specificity as the combination with others, 81% and 91%, respectively. These two genes make up the core of BCa, and were validated in a prospective, blinded study [130].

3.5. Metabolomics and Metabonomics

Metabolites are substrates and products of metabolism that are involved in cellular cycle and influence cell functions [131].
Metabolomics is the profiling of metabolites in biological fluids, cells, and tissue, routinely applied as a biomarker [132].
Metabonomics, on the other hand, is the measurement of changes across the metabolome, with respect to time, due to an intervention [132].
Numerous metabolites have been studied for BCa detection and monitoring, and different methods employed to evaluate their presence and/or alterations in biologic liquids: nuclear magnetic resonance, liquid chromatography, mass spectrometry, electrophoresis and fluorescence are only some of them [133,134,135,136,137,138,139,140]. Raman chemometric urinalysis was proposed as a simple, low-cost and rapid screening test evaluating characteristic BCa metabolic signature, as well as Fourier infrared spectroscopy on the bladder wash, yielding sensitivity of 82.4% and specificity of 79.5% [134,135]. Other substances considered as diagnostic biomarkers are dimethyl amine and valine metabolites, some fatty acids, histidine and retinol metabolites, purine metabolism end-products, peptide and collagen fragments, as well as few essential amino acids that varied greatly in pre- and post- operative period, and their levels were found to be correlated with tumor grade. Overall sensitivities varied from 51–98%, specificities from 51–77.3% and NPV 91.2–93.6% [133,134,135,136,137,138,139,140]. Interestingly, a panel of six ions was also considered, of which the most representative was imidazoleacetic acid, but resulting in lower sensitivity and specificity than other metabolites, and thus, not further investigated [136].
Metabolites can be measured also in patients’ serum, in fact five peptides that were evaluated as a potential BCa serum diagnostic biomarker achieved sensitivity of 93.75% and specificity of 96.77%, but they also need ulterior clinical validation [140].

3.6. Combination of Different Categories

It was hypothesized that the combination of urine exosomal miRNA expressions and alterations in certain genes, as a panel for BCa detection and diagnosis would result in more accuracy when compared to only one category. Amuran et al. proposed measuring concentrations of urinary exosomal miR-19b1-5p, 21-5p, 136-3p, 139-5p, 210-3p expressions and urinary BLCA-4, NMP22, APE1/Ref1, CRK, VIM and creatinine concentrations as a diagnostic panel. They found that it was able to diagnose BCa with 80% sensitivity and 88% specificity as well as differentiate low risk patients from healthy controls [46]. On the other hand, combining only miRNA663 and methylated VIM diagnostic values were slightly inferior, 92.6% sensitivity and 90% NPV [141].
Furthermore, conventional cytology can be bettered by assessing, at the same time, methylation of some genes, such as NID2 and TWIST1 combined, or using the panel of SALL3, CFTR and TWIST1 [142,143] the same as pairing altered methylation with TERT and FGFR3 mutations [144].
The combination of 3 miRNAs and 2 lncRNAs, overly altered in BCa, was also evaluated, but did not display optimal diagnostic features (AUC = 0.910; sensitivity = 87.2% and specificity = 83.3%) [145], the same as coupling metabolomics assay and 5 target mRNAs (Xpert panel) resulting in NPV of 98.2%, not much better than when used singularly [146].
Finally, Fouad et al. tried to assess diagnostic power of protein and gene expression levels of tissue inhibitor of metalloproteinase-2 (TIMP-2), matrix metalloproteinase-2 (MMP-2), and MMP-9 in urine and blood. The researchers reported excellent specificities, sensitivities and NPV, but the panel has not been ulteriorly tested [147].

3.7. Improving Conventional Cytology

It was suggested that by adding the immunohistochemical staining to the cytology its diagnostic value could improve. Courtade-Saidi et al. added coloration for p53 and ki-67 when analyzing cells from bladder, upper urinary tract and neobladder. When comparing specificity, the results were 76.7% for cytology alone, 93.2% for cytology with p53, 88.0% for cytology with Ki-67, and 97.5% for cytology with p53 and Ki-67, without penalizing the sensitivity for all grades. Combined cytology and p53 was better for detecting low-grade tumors, whilst cytology and ki-67 was optimal for detecting high-grade tumors [148]. Other biomarkers proposed as an adjunct to conventional cytology such as CD20 and AMARC, CD20 and p53 or p53 combined with MCM5, MCM3 and ki67, or even dual-labeling ki67/p16, when correlated with final histology, resulted in sensitivity ranging from 80–83.5% and specificity 71.4–74.3%, not outperforming cytology alone by much [148,149,150,151,152,153]. CD20 performed better as the diagnostic biomarker, especially in low-grade (sensitivity 64.3 and specificity 90.0%) [34,154,155], although some Authors found discordant results and suggested cautious interpretation of positive immunohistological staining [155]. On the other hand, it was argued not to use ki-67 when trying to distinguish CIS and florid atypia as it performed poorly [156].
The cytology can also be improved by performing quantitative phase imaging on diffraction phase microscopy by measuring nuclear/cell dry mass. In fact, patients in the suspicious group presented with significantly higher nuclear mass and entropy cells than controls [157]. Finally, staining for ARID1A using immunocytochemistry performed well as a predictive, but not as well as a diagnostic biomarker [158].

4. Conclusions

Patients with hematuria are a significant burden to urology clinics, and despite of the phenomenon frequency, no clear consensus exists on how to diagnose nor treat them. Usually, cystoscopy is performed. Furthermore, American and European guidelines recommend cystoscopy and cytology as a follow-up in non-muscle invasive BCa [3]. Concerns regarding poor sensitivity of cytology and frequent ‘inconclusive’ results [11,23,159,160], as well as not a negligible percentage of rare variants that are often even more difficult to be diagnosed and ambiguous flat lesions reported during cystoscopies, are more present than ever [21,22,23]. These were the main reasons why researchers have shifted their attention into discovering other, noninvasive, low cost, non-observer dependent, non-labor-intensive, rapid and accurate detection tests.
In this review we discussed a significant amount of potential urinary and serum diagnostic biomarkers for bladder cancer, the majority of them in need of further validation in larger, multicentric, blinded and prospective trials. Nevertheless, some have a great potential, with satisfactory negative predictive value, such as, e.g., assays containing multiple pathological gene expression [130]. The summary of all new potential biomarkers is shown in Table 1.
The problem we are facing now, as described in this review, is an enormous variability in sensitivity, specificity and NPV of the same biomarker. This, although justifiable by various factors such as age, sex, lifestyle, number of exfoliated cell in urine, high number of mutational burden and/or presence of hematuria, creates an important confusion between clinicians, those who actually need to use these tests. Moreover, a significant number of proposed biomarkers still needs an external validation, and this is the main reason why results reported in these studies may not be attainable. Another interesting fact to take in consideration is the significant heterogeneity of the bladder cancer, not only interpatient variability, but an intra-tumoral heterogeneity [159,160,161,162]. This means that, based on the exfoliated cells, even the most precise genetic tests could miss a diagnosis [159,161].
The most important factor a novel biomarker should have is a high NPV. The acceptable NPV should be higher than that of cystoscopy, therefore, at least >95% and it should not be operator dependent. In the past, FDA-approved tests (NMP22, BladderChek, UroVysion, EpiCheck, ImmunoCyt, BTA-TRAK and BTA-STAT) have been used, but recently, some decline in their clinical use has been observed, because there is a lack of clear indications on their use, additional cost, complicated processing or even availability problem.
It seems that the most appropriate test should be an assay, a collective of biomarkers that integrates the most frequently mutated genes, with common epigenetic changes, altered superficial proteins and tumor environmental factors, such as immunological response and urinary microbiome, as well as metabolic changes that are tumor induced. However, most importantly, NPV aside, it should be readily available even in the most peripheric urology clinic and it should be cost-efficient.
We reviewed the literature on urinary and serum diagnostic biomarkers present in the last 5 years, so some good biomarkers discovered and validated prior to 2016 may not be included in this paper, which poses a limitation. Nevertheless, we did try to simplify as much as possible the current biomarker landscape, not only to enrich the knowledge of the reader, but also to stimulate young clinicians to be more involved when it comes to novelty, especially if that means that money saved from unnecessary cystoscopies can be redirected to other, more useful channels.
Table
Table 1. Summary of all potential BCa diagnostic biomarkers.
Table 1. Summary of all potential BCa diagnostic biomarkers.
Biomarker CategoryBiomarkerSensitivitySpecificityReferences
Conventional cytology16–100%86%[3]
Altered proteins
Cytokeratins 8 and 18 (UBC rapid test)30–87%63–91%[28,29,30,31,32]
Cytokeratin 2056–76% [33,34]
NMP2237.9–88.5%65.2–96.9%[36]
SPARC39–43%70–78%[38]
Orosomucoid-192%94%[45]
APE1/REF181.7%79.6%[39]
Soluble FAS88.03%89.19%[40]
AURKA79.6%79.7%[41]
Serum Irisin74.7%90.7%[42]
AIB180%86%[44]
EIF5A274%78%[44]
AIB1, EIF5A2, NPM2289%91%[44]
LASP159%80%[47]
Unphosphorylated TF70.6%97.8%[48]
Phosphorylated TF-pSer25888.2%93.3%[48]
Phosphorylated TF-pSer25388.2%24.4%[48]
Multiplex immunoassay—A1AT, APOE, ANG, CA9, IL8, MMP9, MMP10, PAI1, SDC1, VEGFA87–93%93%[49,50,51]
Genomic mutations
TERT promoter mutation46.7–90%90–100%[56]
TERT, FGFR3, KRAS79–98%62–90%[57,58]
TERT and PLEKHS1 promoters91–95%96–100%[62]
TERT, FGFR3, OTX172%59%[60]
FEGFR3, Cyclin D373%90%[59]
FGFR3, TP53, PIK3CA, ARID1A, STAG2, KTM2D73–95%85–90%[61]
ASPM upregulation95.56%86.67%[63]
TEF downregulation91.11%71.11%[64]
MCM5 overexpression63–79%65–88%[65,66,67,68,69,70]
Microsatellite instability (MIS) or Loss of heterozigosity (LOH) of D16S476, D9S171, FGA, ACTBP296.7%30%[72]
Circulating free nucleid acids
Urine-derived fc-DNA20.7%91.2%[74]
Urine exosomes92.6%83.3%[75]
Serum exosomes82.4%100%[75]
5 mRNA mutations panel: MDK, HOXA13, CDC2, IGFB5, CXCR2 (Cxbladder)91–97.7%61–85%[76,77,78,79]
5 mRNAs: CRH, IGF2, UPK1B, ANXA10, ABL1 (Xpert Bladder)63–80%73–81%[80,81,82,83,84,85,86,87,88,89]
UBEC282.5%76.2%[90]
IQGAP380–96.2%60.2–90.7%[91]
N-Myc85.5%81.4%[92]
Epigenetic changes and Transcriptomic alterations
miRNA 130 family (-130a-3p, -130b-3p, -301a-3p)87.8%93.3%[102]
miRNA 19276.7%78%[100]
miRNA 192, 2D ultrasound93.2%96.7%
2 miRNAs panel: -31-5p, -93-5p82%70%[99]
7 miRNAs panel: -7-5p, -22-3p, -29a-3p, -126-5p, -200a-3p, -375, -423-5p80.88%91.67%[98]
12 miRNAs panel: -16, -21, -34a, -99a, -106b, -126, -129, -133a, -145, -200c, -205, -218, -221/222, -33188%48%[103]
miRNA urinary supernatant: -125b, -30b, -204, -99a, -532-3p59%96%[97]
7 serum miRNAs panel: -6087, -6724, -3960, -1343-5p, -1185-1-3p, -6831-5p, -4695-5p95–98%87–91%[101,104]
25 miRNAs panel (-140-5p, -142-5p, -199a-3p, -93, -652, -20a, -106b, -1305, -223, -18a, -191, -126, -26b, -26a, -145, -146a, -30a-3p, -96, -573, -221, -182,-142-3p, -19b, -224, -181a, -766, -146b-5p, -429, -200a, -200c, -20b, -324-3p, -19a, -106a, -143, -99b, -140-3p, -491-5p, -151-3p, -671-3, -222, -339-3p, -141, -200b, -7b, -2187%100%[95]
10 miRNAs panel: -652, -199a-3p, -140-5p, -93, -142-5p, -1305, -30a, -224, -96, -76684%87%
DNA methylation test of 15 (unpublished) genes (Bladder EpiCheck)57–85%80–88%[106,107,109,110,111]
Methylation of tumor suppressor genes (p14ARF, p16INK4A, DAPK, RASSF1A, APC)91–100%NA[117]
DNA hypermethylation of 150 loci panel (UroMark)96%97%[118,119]
Hypermethylation of OTX1, ONECUT2, TWIST1, SEPTIN9, PCDH17, POU4F2, HS3ST2, SLIT2, FGFR3, CFTR, SALL3, GHSR, MAL, mutation of HRAS, TERT, FGFR393–98%40–86%[60,112,113,114,115,116]
RNA sequencing of two cancer specific-genes and one reference gen: ROBO1, WNT5A, CDC42BPB83%89%[121]
Urinary lncRNA uc004cox.480%85%[125]
3 lncRNAs panel: PCAT-1, UBC1 and SNHG1680%75%[122]
3 lncRNAs panel: MALAT1, PCAT1, SPRY4-IT162.5%85%[123]
3 lncRNAs panel: MALAT1, MEG, SNHG1682%73%[124]
3 lnc RNAs panel: PVT-1, ANRIL, PCAT-1 (tested only in T1-T2)46.67%87.5%[126]
7 lncRNAs panel: HOTAIR, NEAT1, TUG1, FAS-AS1, PVT1, GHET1, HOTAIRM1100%100%[127]
4 lncRNAs panel: LINC00355, UCA1-201, UCA1-203, MALAT192%91.7%[128]
3 lncRNAs panel: H19, UCA1 and HOTAIR70.8%88.5%[129]
12 genes panel transcriptomic alterations: IGF2, MAGEA3, KLF9, CRH, SLC1A6, POSTN, TERT, AHNAK2, ANXA10, CTSE, KRT20, PPP1R14D79%93%[130]
10 genes panel transcriptomic alterations: IGF2, MAGEA3, KLF9, CRH, SLC1A6, POSTN, EBF1, CFH, MCM10, MMP1280%94%
5 genes panel transcriptomic alterations: IGF2, MAGEA3, KLF9, CRH, SLC1A679%92%
2 genes transcriptomic alterations: IGF2, MAGEA381%91%
Metabolomics and Metabonomics
Dimethyl amine, malonate, glutamine, lactate, histidine and valine metabolites panel80.8–98.1%66.7–80.3%[133]
Phosphatidylinositol, nucleic acids, collagen, aromatic amino acids, cholesterol fatty acids, glycogen, monosaccarides and carotenoids’ changes (Rametrix)82.4%79.5%[134]
Bladder wash (>resectisol, urea, creatinine, uric acid, different types of cells, cylinders, crystals) analyzed by FTIR81.8–100%52.9–80.9%[135]
6 ions panel (>imidazoleacetic acid)82%85–90%[136]
116 peptides panel (>collagen fragments, APO-I peptides, basement-membrane specific heparan proteoglycan fragments)88–91%51–68%[138]
3 serum metabolites panel: Inosine, PS(O-18:0/0:0), Acetyl-N-formyl-5-methoxykynurenamine84.6%84.6%[137]
Concentration matrices55%74.7%[139]
Combination of more than one category
miRNAs: -19b1-5p, 21-5p, 136-3p, -139-5p, 210-3p combined with BLCA-4, NMP22, APE1/Ref1, CRK, VIM, and creatinine urinary concentrations80%88%[46]
miRNA-663, VIM hypermethylation92.6%90%[141]
Altered methylation of CFTR, SALL3, TWIST 1, NID2, TWIST1 in adjunct to Cytology57–96%40–72%[142,143]
Altered methylation of SALL3,ONECUT2, CCNA1,BCL2, EOMES, VIMcombined with altered mutations of TERT, FGFR397%77%[144]
3 mRNAs: KLHDC7B, CASP14 and PRSS1; lncRNAs: MIR205HG and GAS587.2%83.3%[145]
Metabolomics (CRAT and SLC 25A20 genes expression) + 5 mRNAs: CRH, IGF2, UPK1B, ANXA10, ABL166%98%[146]
MMP-2, MMP-9 and TIMP-2 urinary and serum proteins’ and genes’ expression levels100%100%[147]
Improving cytology
Cytology and CK20 immunostaining64.3–97%80–90%[150,154]
Cytology with CK20 and p53 immunostaining91.1%74.3%[149,156]
Cytology enriched with p53, ki67 coloration68.9%97.5%[148,155]
Cytology combined with AMARC coloration73%97%[150]
Cytology combined with p16/ki-67 dual-labeling80%71.4%[151]
p53, MCM5, MCM2, ki-67 coloration in adjunct to cytology67.3–90.4%72–80%[153]
Cytology combined with loss of ARID1A expressionNANA[158]

Author Contributions

R.M. and G.M.: conceptualization and methodology; L.L.M.: data curation; R.M.: writing—original draft preparation; F.M.: visualization, investigation; R.L. and A.V.d.M.: supervision; A.S. and C.T.: validation; R.M.: writing—reviewing and editing. All authors have read and agreed to the published version of the manuscript.

Funding

The authors received no financial support for the research, authorship and publication of this article.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Aronson, J.K.; Ferner, R.E. Biomarkers—A General Review. Curr. Protoc. Pharmacol. 2017, 76, 9.23.1–9.23.17. [Google Scholar] [CrossRef] [PubMed]
  2. Califf, R.M. Biomarker definitions and their applications. Exp. Biol. Med. 2018, 243, 213–221. [Google Scholar] [CrossRef] [PubMed]
  3. Babjuk, M.; Burger, M.; Capoun, O.; Cohen, D.; Comperat, E.M.; Dominguez Escrig, J.L.; Gontero, P.; Liedberg, F.; Masson-Lecomte, A.; Mostafid, A.H.; et al. EAU Guidelines 2021 on NMIBC (Oppure MIBC). Edn. Presented at the EAU Annual Congress Milan 2021. EAU Guidelines Office: Arnhem, The Netherlands. Available online: https://uroweb.org/guideline/non-muscle-invasive-bladder-cancer/ (accessed on 30 November 2021).
  4. Cambier, S.; Sylvester, R.J.; Collette, L.; Gontero, P.; Brausi, M.A.; van Andel, G.; Kirkels, W.J.; Calais Da Silva, F.; Oosterlinck, W.; Prescott, S.; et al. EORTC Nomograms and Risk Groups for Predicting Recurrence, Progression, and Disease-specific and Overall Survival in Non-Muscle-invasive Stage Ta-T1 Urothelial Bladder Cancer Patients Treated with 1–3 Years of Maintenance Bacillus Calmette-Guerin. Eur. Urol. 2016, 69, 60–69. [Google Scholar] [CrossRef] [PubMed]
  5. Sylvester, R.J.; van der Meijden, A.P.M.; Oosterlinck, W.; Witjes, A.J.; Bouffioux, C.; Denis, L.; Newling, D.W.W.; Kurth, K. Predicting recurrence and progression in individual patients with stage Ta T1 bladder cancer using EORTC risk tables: A combined analysis of 2596 patients from seven EORTC trials. Eur. Urol. 2006, 49, 466–477. [Google Scholar] [CrossRef]
  6. Tan, W.S.; Rodney, S.; Lamb, B.; Feneley, M.; Kelly, J. Management of non-muscle invasive bladder cancer: A comprehensive analysis of guidelines from the United States, Europe and Asia. Cancer Treat. Rev. 2016, 47, 22–31. [Google Scholar] [CrossRef]
  7. Ferlay, J.; Soerjomataram, I.; Dikshit, R.; Eser, S.; Mathers, C.; Rebelo, M.; Parkin, D.M.; Forman, D.; Bray, F. Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012. Int. J. Cancer 2015, 136, E359–E386. [Google Scholar] [CrossRef]
  8. Yafi, F.A.; Brimo, F.; Steinberg, J.; Aprikian, A.G.; 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. [Google Scholar] [CrossRef]
  9. Raitanen, M.P.; Aine, R.; Rintala, E.; Kallio, J.; Rajala, P.; Juusela, H.; Tammela, T.L.J.; Group, F. Differences between local and review urinary cytology in diagnosis of bladder cancer. An interobserver multicenter analysis. Eur. Urol. 2002, 41, 284. [Google Scholar] [CrossRef]
  10. Witjes, J.A.; Moonen, P.M.J.; van der Heijden, A.G. Review pathology in a diagnostic bladder cancer trial: Effect of patient risk category. Urology 2006, 67, 751–755. [Google Scholar] [CrossRef]
  11. Karakiewicz, P.I.; Benayoun, S.; Zippe, C.; Ludecke, G.; Boman, H.; Sanchez-Carbayo, M.; Casella, R.; Mian, C.; Friedrich, M.G.; Eissa, S.; et al. Institutional variability in the accuracy of urinary cytology for predicting recurrence of transitional cell carcinoma of the bladder. BJU Int. 2006, 97, 997. [Google Scholar] [CrossRef]
  12. Vanden Bussche, C.J.; Allison, D.B.; Gupta, M.; Ali, S.Z.; Rosenthal, D.L. 20-year and 46,000-specimen journey to Paris reveals the influence of reporting systems and passive peer feedback on pathologist practice patterns. Cancer Cytopathol. 2018, 126, 381–389. [Google Scholar] [CrossRef] [PubMed]
  13. Osch, F.H.M.; Nekeman, D.; Aaronson, N.K.; Billingham, L.J.; James, N.D.; Cheng, K.K.; Bryan, R.T.; Zeegers, M.P. Patients choose certainty over burden in bladder cancer surveillance. World J. Urol. 2019, 37, 2747–2753. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Ten, W.S.; Teo, C.H.; Chan, D.; Heinrich, M.; Feber, A.; Sarpong, R.; Allan, J.; Williams, N.; Brew-Graves, C.; Jenn Ng, C.; et al. Mixed-methods approach to exploring patients’ perspectives on the acceptability of a urinary biomarker test in replacing cystoscopy for bladder cancer surveillance. BJU Int. 2019, 124, 408–417. [Google Scholar] [CrossRef] [PubMed]
  15. de Bekker-Grob, E.W.; van der Aa, M.N.M.; Zwarthoff, E.C.; Eijkemans, M.J.C.; van Rhijn, B.W.; van der Kwast, T.H.; Steyerberg, E.W. Non-muscle-invasive bladder cancer surveillance for which cystoscopy is partly replaced by microsatellite analysis of urine: A cost-effective alternative? BJU Int. 2009, 104, 41–47. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Georgieva, M.V.; Wheeler, S.B.; Erim, D.; Smith-Bindman, R.; Loo, R.; Ng, C.; Garg, T.; Raynor, M.; Nielsen, M.E. Comparison of the harms, advantages, and costs associated with alternative guidelines for the evaluation of hematuria. JAMA Intern. Med. 2019, 179, 1352–1362. [Google Scholar] [CrossRef]
  17. Han, D.S.; Lynch, K.E.; Chang, W.J.; Sirovich, B.; Robertson, D.J.; Swanton, A.R.; Seigne, J.D.; Goodney, P.P.; Schroeck, F.R. Overuse of Cystoscopic surveillance among patients with low-risk non-muscle-invasive bladder Cancer-a National Study of patient, provider, and facility factors. Urology 2019, 131, 112–119. [Google Scholar] [CrossRef]
  18. Schroeck, F.R.; Lynch, K.E.; Li, Z.; MacKenzie, T.A.; Han, D.S.; Seigne, J.D.; Robertson, D.J.; Sirovich, B.; Goodney, P.P. The impact of frequent cystoscopy on surgical care and cancer outcomes among patients with low-risk, nonmuscle-invasive bladder cancer. Cancer 2019, 125, 3147–3154. [Google Scholar] [CrossRef]
  19. Nabi, G.; Greene, D.R.; O’Donnell, M. How important is urinary cytology in the diagnosis of urological malignancies? Eur. Urol. 2003, 43, 632–636. [Google Scholar] [CrossRef]
  20. Strittmatter, F.; Buchner, A.; Karl, A.; Sommer, M.; Straub, J.; Tilki, D.; Hennenberg, M.; Knuechel, R.; Stief, C.G.; Zaak, D.; et al. Individual learning curve reduces the clinical value of urinary cytology. Clin. Genitourin. Cancer 2011, 9, 22–26. [Google Scholar] [CrossRef]
  21. Danakas, A.; Sweeney, M.; Cheris, S.; Agrawal, T. Urinary tract cytology: A cytologic-histopatologic correlation with The Paris System, an institutional study. J. Am. Soc Cytopathol. 2021, 10, 56–63. [Google Scholar] [CrossRef]
  22. Paez, A.; Coba, J.M.; Murillo, N.; Fernandez, P.; de la Cal, M.A.; Lujan, M.; Berenguer, A. Reliability of the routine cytological diagnosis in bladder cancer. Eur. Urol. 1999, 35, 228–232. [Google Scholar] [PubMed]
  23. Palou, J.; Brausi, M.; Catto, J.W.F. Management of Patients with Normal Cystoscopy but Positive Cytology or Urine Markers. Eur. Urol. Oncol. 2020, 3, 548–554. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Available online: http://www.prisma-statement.org/PRISMAStatement/FlowDiagram (accessed on 8 July 2021).
  25. Shimada, K.; Fujii, T.; Tatsumi, Y.; Anai, S.; Fujimoto, K.; Noboru, K. Ubiquilin2 as a novel marker for detection of urothelial carcinoma cells in urine. Diagn. Cytopathol. 2016, 44, 3–9. [Google Scholar] [CrossRef] [PubMed]
  26. Hwee, T.T.; Lee, Y.H.; Chung, M.C.M. Cancer proteomics. Mass Spectrom Rev. 2012, 31, 583–605. [Google Scholar]
  27. Weng, Y.R. Biological functions of cytokeratin 18 in cancer. Mol. Cancer Res. 2012, 10, 485–493. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  28. Styrke, J.; Henriksson, H.; Ljungberg, B.; Hasan, M.; Silfverberg, I.; Einarsson, R.; Malmstrom, P.; Sherif, A. Evaluation of the diagnostic accuracy of UBC (R) Rapid in bladder cancer: A Swedish multicentre study. Scand J. Urol. 2017, 51, 293–300. [Google Scholar] [CrossRef] [PubMed]
  29. Ecke, T.H.; Weib, S.; Stephan, C.; Hallmann, S.; Barski, D.; Otto, T.; Gerullis, H. UBC (R) Rapid Test for detection of carcinoma in situ for bladder cancer. Tumour. Biol. 2017, 39, 1010428317701624. [Google Scholar] [CrossRef] [Green Version]
  30. Pichler, R.; Tulchiner, G.; Fritz, J.; Shaefer, G.; Horninger, W.; Heidegger, I. Urinary UBC Rapid and NMP22 Test for Bladder Cancer Surveillance in Comparison to Urinary Cytology: Results from a Prospective Single-Center Study. Int. J. Med. Sci. 2017, 14, 811–819. [Google Scholar] [CrossRef] [Green Version]
  31. Barak, V.; Itzkovich, D.; Einarsson, R.; Gofrit, O.; Pode, D. Non-invasive Detection of Bladder Cancer by UBC Rapid test, Ultrasonography and Cytology. Anticancer Res. 2020, 40, 3967–3972. [Google Scholar] [CrossRef]
  32. Gleichenhagen, J.; Arndt, C.; Casjens, S.; Meinig, C.; Gerullis, H.; Raiko, I.; Bruning, T.; Ecke, T.; Johnen, G. Evaluation of a New Survivin ELISA and UBC® Rapid for the Detection of Bladder Cancer in Urine. Int. J. Mol. Sci. 2018, 19, 226. [Google Scholar] [CrossRef] [Green Version]
  33. Salomo, K.; Huebner, D.; Boehme, M.U.; Herr, A.; Brabetz, W.; Heberling, U.; Hakenberg, O.W.; Jahn, D.; Grimm, M.; Steinbach, D.; et al. Urinary transcription of CK20 and IGF2 for the non-invasive bladder cancer detection. J. Cancer Res. Clin. Oncol. 2017, 143, 1757–1769. [Google Scholar] [CrossRef] [PubMed]
  34. Wadhwa, N.; Diwaker, P.; Lotha, N.; Arora, V.K.; Singh, N. Cytokeratin 20 immunocytochemistry on urine sediments: A potential low-cost adjunct to cytology in the diagnosis of low-grade urothelial carcinoma. Cytopathology 2017, 28, 531–535. [Google Scholar] [CrossRef] [PubMed]
  35. Albrethsen, J.; Knol, J.C.; Jimenez, C.R. Unravelling the nuclear matrix proteome. J. Proteom. 2009, 72, 71–81. [Google Scholar] [CrossRef]
  36. Xia, C.S.; Fan, C.; Su, M.; Wang, Q.; Bao, H. Use of the Nuclear Matrix Protein 22 BladderChek Test for the Detection of Primary and Recurrent Urothelial Carcinoma. Dis. Markers 2020, 2020, 3424039. [Google Scholar] [CrossRef] [PubMed]
  37. Luo, Z.; Lei, H.; Sun, Y.; Liu, X.; Su, D. Orosomucoid, an acute response protein with multiple modulating activities. J. Physiol. Biochem. 2015, 71, 329–340. [Google Scholar] [CrossRef]
  38. Cristelis, E.; Rava, M.; Marquez, M.; Lygirou, V.; Chatzicharalambous, D.; Liapi, P.; Lichtinghagen, R.; Brand, K.; Cecchini, L.; Vlahou, A.; et al. Diagnostic and Prognostic Performance of Secreted Protein Acidic and Rich in Cysteine (SPARC) Assay for Detecting Primary and Recurrent Urinary Bladder Cancer. Proteom. Clin. Appl. 2019, 13, e1800148. [Google Scholar]
  39. Choi, S.; Shin, J.H.; Lee, Y.R.; Joo, H.K.; Song, K.H.; Na, Y.G.; Chang, S.J.; Lim, J.S.; Jeon, B.H. Urinary APE1/Ref-1: A Potential Bladder Cancer Biomarker. Dis. Markers 2016, 2016, 7276502. [Google Scholar] [CrossRef] [Green Version]
  40. Srivastava, A.K.; Singh, P.K.; Singh, D.; Dalela, D.; Rath, S.K.; Bhatt, M.L.B. Clinical utility of urinary soluble Fas in screening for bladder cancer. Asia Pac. J. Clin. Oncol. 2016, 12, 27. [Google Scholar] [CrossRef]
  41. Mobley, A.; Zhang, S.; Bondaruk, J.; Wang, Y.; Majewski, T.; Caraway, N.P.; Huang, L.; Shoshan, E.; Velazquez-Torres, G.; Nitti, G.; et al. Aurora Kinase A is a Biomarker for Bladder Cancer Detection and Contributes to its Aggressive Behavior. Sci. Rep. 2017, 7, 40714. [Google Scholar] [CrossRef]
  42. Esawy, M.M.; Abdel-Samd, K.M. The diagnostic and prognostic roles of serum irisin in bladder cancer. Curr. Probl. Cancer 2020, 44, 100529. [Google Scholar] [CrossRef]
  43. Lahusen, T.; Henke, R.T.; Kagan, B.L.; Wellstein, A.; Riegel, A.T. The role and regulation of the nuclear receptor co-activator AIB1 in breast cancer. Breast Cancer Res. Treat. 2009, 116, 225–237. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Zhou, B.F.; Wei, J.; Chen, Z.; Dong, P.; Lai, Y.; Fang, Y.; Jiang, H.; Lu, J.; Zhou, F.; Xie, D.; et al. Identification and validation of AIB1 and EIF5A2 for noninvasive detection of bladder cancer in urine samples. Oncotarget 2016, 7, 41703–41714. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Li, F.; Yu, Z.; Chen, P.; Lin, G.; Li, T.; Hou, L.; Yuejun, D.; Tan, W. The increased excretion of urinary orosomucoid 1 as a useful biomarker for bladder cancer. Am. J. Cancer Res. 2016, 6, 331–340. [Google Scholar]
  46. Amuran, G.G.; Tinay, I.; Filinte, D.; Ilgin, C.; Eyuboglu, I.P.; Akkiprik, M. Urinary micro-RNA expressions and protein concentrations may differentiate bladder cancer patients from healthy controls. Int. Urol. Nephrol. 2020, 52, 461–468. [Google Scholar] [CrossRef] [PubMed]
  47. Butt, E.; Ebbing, J.; Bubendorf, L.; Ardelt, P. Influence of hematuria and infection on diagnostic accuracy of urinary LASP1: A new biomarker for bladder carcinoma. Biomark Med. 2017, 11, 347–357. [Google Scholar] [CrossRef] [PubMed]
  48. Greenfield, R.S.; Herd, T.M.; Date, K.; Cooper, P.; O’Kane, A.; Gardiner, E.; Maraveyas, A. Signal Transduction Peptide of Tissue Factor Phosphorylated at Ser258 and the Unphosphorylated STP in Urine Are Potential Biomarkers for Bladder Cancer. Clin Genitourin Cancer 2019, 17, e247–e257. [Google Scholar] [CrossRef]
  49. Hirasawa, Y.; Pagano, I.; Chen, R.; Sun, Y.; Dai, Y.; Gupta, A.; Tikhonenkov, S.; Goodison, S.; Rosser, C.J.; Furuya, H. Diagnostic performance of Oncuria™, a urinalysis test for bladder cancer. J. Transl. Med. 2021, 19, 141. [Google Scholar] [CrossRef]
  50. Goodison, S.; Ogawa, O.; Matsui, Y.; Kobayashi, T.; Miyake, M.; Ohnishi, S.; Fujimoto, K.; Dai, Y.; Shimizu, Y.; Tsukikawa, K.; et al. A multiplex urinary immunoassay for bladder cancer detection: Analysis of a Japanese cohort. J. Transl. Med. 2016, 14, 287. [Google Scholar] [CrossRef] [Green Version]
  51. Shimizu, Y.; Furuya, H.; Greenwood, P.B.; Chan, O.; Dai, Y.; Thoornquist, M.D.; Goodison, S.; Rosser, C.J. A multiplex immunoassay for the non-invasive detection of bladder cancer. J. Transl. Med. 2016, 14, 016–0783. [Google Scholar] [CrossRef] [Green Version]
  52. Martincorena, I.; Raine, K.M.; Gerstung, M.; Dawson, K.J.; Haase, K.; van Loo, P.; Davies, H.; Stratton, M.R.; Campbell, P.J. Universal patterns of selection in cancer and somatic tissues. Cell 2017, 171, 1029–1041.e21. [Google Scholar] [CrossRef]
  53. A practical Guide to The Cancer Genome Atlas (TCGA). Available online: https://link.springer.com/protocol/10.1007/978-1-4939-3578-9_6 (accessed on 8 July 2021).
  54. Patel, B.; Taiwoo, R.; Kim, A.H.; Dunn, G.P. TERT, a promoter of CNS malignancies. Neurooncol. Adv. 2020, 2, vdaa025. [Google Scholar] [CrossRef] [PubMed]
  55. Garje, R.; An, J.; Obeidat, M.; Kumar, K.; Yasin, H.A.; Zakharia, Y. Fibroblast Growth Factor Receptor (FGFR) Inhibitors in Urothelial Cancer. Oncologist 2020, 25, e1711–e1719. [Google Scholar] [CrossRef] [PubMed]
  56. Critelli, R.; Fasanelli, F.; Oderda, M.; Polidoro, S.; Assumma, M.B.; Viberti, C.; Preto, M.; Gontero, P.; Cucchiarale, G.; Lurkin, I.; et al. Detection of multiple mutations in urinary exfoliated cells from male bladder cancer patients at diagnosis and during follow-up. Oncotarget 2016, 7, 67435–67448. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  57. Batista, R.; Vinagre, J.; Prazeres, H.; Sampaio, C.; Peralta, P.; Conceicao, P.; Sismeiro, A.; Leao, R.; Gomes, A.; Furriel, F.; et al. Validation of a Novel, Sensitive, and Specific Urine-Based Test for Recurrence Surveillance of Patients with Non-Muscle-Invasive Bladder Cancer in a Comprehensive Multicenter Study. Front. Genet. 2019, 10, 1237. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Sieverink, C.A.; Batista, R.P.M.; Prazeres, H.J.M.; Vinagre, J.; Sampaio, C.; Leao, R.R.; Maximo, V.; Witjes, J.A.; Soares, P. Clinical validation of a urine test (uromonitor-V2) for the surveillance of non-muscle-invasive bladder cancer patients. Diagnostics 2020, 10, 745. [Google Scholar] [CrossRef]
  59. Blanca, A.; Requena, M.J.; Alvarez, J.; Cheng, L.; Montironi, R.; Raspollini, M.R.; Reymundo, C.; Lopez-Beltran, A. FGFR3 and Cyclin D3 as urine biomarkers of bladder cancer recurrence. Biomark Med. 2016, 10, 243–253. [Google Scholar] [CrossRef]
  60. Beukers, W.; van der Keur, K.; Kandimalla, R.; Vergouwe, Y.; Steyerberg, E.W.; Boormans, J.L.; Jensen, J.B.; Lorente, J.A.; Real, F.X.; Segersten, U.; et al. FGFR3, TERT and OTX1 as a Urinary Biomarker Combination for Surveillance of Patients with Bladder Cancer in a Large Prospective Multicenter Study. J. Urol. 2017, 197, 1410–1418. [Google Scholar] [CrossRef]
  61. Feng, Z.; Zhang, Y.; Shi, L.; Wu, C.; Chen, S.; Zheng, H.; Song, D. Gene mutation detection of urinary sediment cells for NMIBC early diagnose and prediction of NMIBC relapse after surgery. Medicine 2019, 98, e16451. [Google Scholar]
  62. Dudley, J.C.; Schroers-Martin, J.; Lazzareschi, D.V.; Shi, W.Y.; Chen, S.B.; Esfahani, M.S.; Trivedi, D.; Chabon, J.J.; Chaudhuri, A.A.; Stehr, H.; et al. Detection and Surveillance of Bladder Cancer Using Urine Tumor DNA. Cancer Discov. 2019, 9, 500–509. [Google Scholar] [CrossRef]
  63. Pai, V.C.; Hsu, C.; Chan, T.; Liao, W.; Chuu, C.; Chen, W.; Li, C.; Lin, C.; Huang, S.; Chen, L.; et al. ASPM promotes prostate cancer stemness and progression by augmenting Wnt-Dvl-3-β-catenin signaling. Oncogene 2019, 38, 1340–1353. [Google Scholar] [CrossRef]
  64. Saleh, A.; Gohar, S.F.; Hemida, A.S.; Elgharbawy, M.; Soliman, S.E. Evaluation of ASPM and TEF Gene Expressions as Potential Biomarkers for Bladder Cancer. Biochem. Genet 2020, 58, 490–507. [Google Scholar] [CrossRef] [PubMed]
  65. Gontero, P.; Montanari, E.; Roupert, M.; Longo, F.; Stockley, J.; Kennedy, A.; Rodriguez, O.; McCracken, S.R.C.; Dudderidge, T.; Sieverink, C.; et al. Comparison of the performances of the ADXBLADDER test and urinary cytology in the follow-up of non-muscle-invasive bladder cancer: A blinded prospective multicentric study. BJU Int. 2021, 127, 198–204. [Google Scholar] [CrossRef] [PubMed]
  66. Trenti, E.; Pycha, S.; Mian, C.; Schwienbacher, C.; Hanspeter, E.; Kafka, M.; Spedicato, G.A.; Vjaters, E.; Degener, S.; Pycha, A.; et al. Comparison of 2 new realtime polymerase chain reaction−based urinary markers in the followup of patients with non−muscleinvasive bladder cancer. Cancer Cytopathol. 2020, 128, 341–347. [Google Scholar] [CrossRef]
  67. Dudderidge, T.; Stockley, J.; Nabi, G.; Mom, J.; Umez-Eronini, N.; Hrouda, D.; Cresswell, J.; Crozier-McCracken, S.R. A novel, non-invasive test enabling bladder cancer detection in urine sediment of patients presenting with haematuria—a prospective multicentre performance evaluation of ADXBLADDER. Eur. Urol. Oncol. 2019, 3, 42–46. [Google Scholar] [CrossRef]
  68. Anastasi, E.; Maggi, M.; Tartaglione, S.; Angeloni, A.; Gennarini, G.; Leoncini, P.P.; Sperduti, I.; Di Lascio, G.; De Stefano, V.; Di Pierro, G.B.; et al. Predictive value of MCM5 (ADXBLADDER) analysis in urine of men evaluated for the initial diagnosis of bladder cancer: A comparative prospective study. Diagn Cytopathol. 2020, 48, 1034–1040. [Google Scholar] [CrossRef]
  69. Roupret, M.; Gontero, P.; McCracken, S.R.C.; Dudderidge, T.; Stockley, J.; Kennedy, A.; Rodriguez, O.; Sieverink, C.; Vanie, F.; Allasia, M.; et al. Diagnostic Accuracy of MCM5 for the Detection of Recurrence in Nonmuscle Invasive Bladder Cancer Followup: A Blinded, Prospective Cohort, Multicenter European Study. J. Urol. 2020, 204, 685–690. [Google Scholar] [CrossRef] [PubMed]
  70. Bialek, L.; Czerwinska, K.; Fus, L.; Krajewski, W.; Sadowska, A.; Radziszewski, P.; Dobruch, J.; Kryst, P.; Poletajew, S. MCM5 urine expression (ADXBLADDER) is a reliable biomarker of high-risk non- muscle-invasive bladder cancer recurrence: A prospective matched case-control study. Cancer Biomark 2021, 30, 139–143. [Google Scholar] [CrossRef]
  71. Watson, M.M.C.; Berg, M.; Soreide, K. Prevalence and implications of elevated microsatellite alterations at selected tetranucleotides in cancer. Br. J. Cancer 2014, 111, 823–827. [Google Scholar] [CrossRef] [Green Version]
  72. Zekri, A.N.; Khaled, M.H.; Mohammed, M.B.; Diab, F.M.; Abdellateif, M.S.; El Deeb, S.; Badr, M.A.; Mohanad, M.; Abdallah, S.O.; Bahnassy, A.A. Microsatellite instability profiling in Egyptian bladder cancer patients: A pilot study. Curr. Probl. Cancer 2019, 43, 100472. [Google Scholar] [CrossRef]
  73. Pös, O.; Biro, O.; Szemes, T.; Nagy, B. Circulating cell-free nucleic acids: Characteristics and applications. Eur. J. Hum. Genet 2018, 26, 937–945. [Google Scholar] [CrossRef] [Green Version]
  74. Brisuda, A.; Pazourkova, E.; Soukup, V.; Horinek, A.; Hrbacek, J.; Capoun, O.; Svobodova, I.; Pospisilova, S.; Korabecna, M.; Mares, M.; et al. Urinary Cell-Free DNA Quantification as Non-Invasive Biomarker in Patients with Bladder Cancer. Urol. Int. 2016, 96, 25–31. [Google Scholar] [CrossRef] [PubMed]
  75. Elsharkawi, F.; Elsabah, M.; Shabayek, M.; Khaled, H. Urine and Serum Exosomes as Novel Biomarkers in Detection of Bladder Cancer. Asian Pac. J. Cancer Prev. 2019, 20, 2219–2224. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  76. Koya, M.; Osborne, S.; Chemasle, C.; Porten, S.; Schuckman, A.; Kennedy-Smith, A. An evaluation of the real world use and clinical utility of the Cxbladder Monitor assay in the follow-up of patients previously treated for bladder cancer. BMC Urol. 2020, 20, 12. [Google Scholar] [CrossRef] [PubMed]
  77. Konety, B.; Shore, N.; Kader, A.K.; Porten, S.; Daneshmend, S.; Lough, T.; Lotan, Y. Evaluation of Cxbladder and Adjudication of Atypical Cytology and Equivocal Cystoscopy. Eur. Urol. 2019, 76, 238–243. [Google Scholar] [CrossRef] [Green Version]
  78. Davidson, P.J.; McGeoch, G.; Shand, B. Inclusion of molecular marker of bladder cancer in a clinical pathway for investigation of hematuria may reduce the need for cystoscopy. N. Z. Med. J. 2019, 132, 55–64. [Google Scholar]
  79. Lotan, Y.; O’Sullivan, P.; Raman, D.J.; Shariat, S.F.; Kavalieris, L.; Frampton, C.; Guilford, P.; Luxmanan, C.; Suttie, J.; Crist, H.; et al. Clinical comparison of noninvasive urine tests for rulling out recurrent urothelial carcinoma. Urol. Oncol. Semin. Orig. Inesti. 2017, 35, e15–e22. [Google Scholar]
  80. Cancel-Tassin, G.; Roupert, M.; Pinar, U.; Gaffory, C.; Vanie, F.; Ondet, V.; Comperat, E.; Cussenot, O. Assessment of Xpert Bladder Cancer Monitor test performance for the detection of recurrence during non-muscle invasive bladder cancer follow-up. World J. Urol. 2021, 39, 3329–3335. [Google Scholar] [CrossRef]
  81. Elia, C.D.; Pycha, A.; Folchini, D.M.; Mian, C.; Hanspeter, E.; Schwienbacher, C.; Vjaters, E.; Pycha, A.; Trenti, E. Diagnostic predictive value of Xpert Bladder Cancer Monitor in the follow-up of patients affected by non-muscle invasive bladder cancer. J. Clin. Pathol. 2019, 72, 140–144. [Google Scholar]
  82. Cowan, B.; Klein, E.; Jansz, K.; Westenfelder, K.; Bradford, T.; Peterson, C.; Scherr, D.; Karsh, L.I.; Egerdie, B.; Witjes, A.; et al. Longitudinal Follow-up and Performance Validation of a mRNA-based Urine Test (Xpert Bladder Cancer Monitor) for surveillance in Non-Muscle Invasive Bladder Cancer. BJU Int. 2021, 128, 713–721. [Google Scholar] [CrossRef]
  83. Elsawy, A.A.; Awadalla, A.; Elsayed, A.; Abdullateef, M.; Abol-Enein, H. Prospective Validation of Clinical Usefulness of a Novel mRNA-based Urine Test (Xpert Bladder Cancer Monitor) for surveillance in Non Muscle Invasive Bladder Cancer. Urol. Oncol. 2021, 39, 77.e9–77.e16. [Google Scholar] [CrossRef]
  84. Elsawy, A.A.; Awadalla, A.; Abdullateef, M.; Ahmed, A.E.; Abol-Enein, H. Can repeat biopsy be skipped after initial complete resection of T1 bladder cancer? The role of a novel urinary mRNA biomarker. Urol. Oncol. Semin. Orig. Investig 2021, 29, 437.e11–437.e19. [Google Scholar] [CrossRef] [PubMed]
  85. Elia, C.D.; Folchini, D.M.; Mian, C.; Hanspeter, E.; Schwienbacher, C.; Spedicato, G.A.; Pycha, S.; Vjaters, E.; Degener, S.; Kafka, M.; et al. Diagnostic value of Xpert Bladder Cancer Monitor in the follow-up of patients affected by non-muscle invasive bladder cancer: An update. Ther. Adv. Urol. 2021, 13. [Google Scholar] [CrossRef]
  86. Hurle, R.; Casale, P.; Saita, A.; Colombo, P.; Elefante, G.M.; Lughezzani, G.; Fasulo, V.; Paciotti, M.; Domanico, L.; Bevilacqua, G.; et al. Clinical performance of Xpert Bladder Cancer (BC) Monitor, a mRNA-based urine test, in active surveillance (AS) patients with recurrent non-muscle-invasive bladder cancer (NMIBC): Results from the Bladder Cancer Italian Active Surveillance (BIAS) project. World J. Urol. 2020, 38, 2215–2220. [Google Scholar] [CrossRef] [PubMed]
  87. Pichler, R.; Fritz, J.; Tulchiner, G.; Klingmaair, G.; Soleiman, A.; Horninger, W.; Klocker, H.; Heidegger, I. Increased accuracy of a novel mRNA-based urine test for bladder cancer surveillance. BJU Int. 2018, 121, 29–37. [Google Scholar] [CrossRef] [Green Version]
  88. Smrkolj, T.; Cegovnik Primozic, U.; Fabjan, T.; Sterpin, S.; Osredkar, J. The performance of the Xpert Bladder Cancer Monitor Test and voided urinary cytology in the follow-up of urinary bladder tumors. Radiol. Oncol. 2020, 55, 196–202. [Google Scholar] [CrossRef]
  89. van Valenberg, F.J.P.; Hiar, A.M.; Wallace, E.; Bridge, J.A.; Mayne, D.J.; Beqaj, S.; Sexton, W.J.; Lotan, Y.; Weizer, A.Z.; Jansz, G.K.; et al. Prospective Validation of an mRNA-based Urine Test for Surveillance of Patients with Bladder Cancer. Eur. Urol. 2018, 75, 853–860. [Google Scholar] [CrossRef]
  90. Kim, W.T.; Jeong, P.; Yan, C.; Kim, Y.H.; Lee, I.; Kang, H.; Kim, Y.; Lee, S.; Kim, S.; Kim, Y.; et al. UBE2C cell-free RNA in urine can discriminate between bladder cancer and hematuria. Oncotarget 2016, 7, 58193–58202. [Google Scholar] [CrossRef] [Green Version]
  91. Kim, W.T.; Kim, Y.H.; Pildu, J.; Seo, S.; Kang, H.; Kim, Y.; Seok, J.Y.; Lee, S.; Moon, S.; Choi, Y.; et al. Urinary cell-free nucleic acid IQGAP3: A new non-invasive diagnostic marker for bladder cancer. Oncotarget 2018, 9, 14354–14365. [Google Scholar] [CrossRef] [Green Version]
  92. Zhang, M.; Ren, B.; Li, Z.; Niu, W.; Wang, Y. Expression of N-Myc Downstream-Regulated Gene 2 in Bladder Cancer and Its Potential Utility as a Urinary Diagnostic Biomarker. Med. Sci. Monit. 2017, 23, 4644–4649. [Google Scholar] [CrossRef] [Green Version]
  93. O’Brien, J.; Hayder, H.; Zayed, Y.; Peng, C. Overview of MicroRNA Biogenesis, Mechanisms of Actions, and Circulation. Front. Endocrinol. 2018, 9, 402. [Google Scholar] [CrossRef] [Green Version]
  94. Pardini, B.; Cordero, F.; Naccarati, A.; Viberti, C.; Birolo, G.; Oderda, M.; Di Gaetano, C.; Arigoni, M.; Martina, F.; Calogero, R.A.; et al. microRNA profiles in urine by next-generation sequencing can stratify bladder cancer subtypes. Oncotarget 2018, 9, 20658–20669. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  95. Urquidi, V.; Netherton, M.; Gomes-Giacoia, E.; Serie, D.J.; Eckel-Passow, J.; Rosser, C.J.; Goodison, S. A microRNA biomarker panel for the non-invasive detection of bladder cancer. Oncotarget 2016, 7, 86290–86299. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  96. Matullo, G.; Naccarati, A.; Pardini, B. MicroRNA expression profiling in bladder cancer: The challenge of next-generation sequencing in tissues and biofluids. Int. J. Cancer 2016, 138, 2334–2345. [Google Scholar] [CrossRef]
  97. Pospisilova, S.; Pazourkova, E.; Horinek, A.; Brisuda, A.; Svobodova, I.; Soukup, V.; Hrbacek, J.; Capoun, O.; Hanus, T.; Mares, J.; et al. MicroRNAs in urine supernatant as potential non-invasive markers for bladder cancer detection. Neoplasma 2016, 63, 799–808. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  98. Du, L.; Jiang, X.; Duan, W.; Wang, R.; Wang, L.; Zheng, G.; Yan, K.; Wang, L.; Li, J.; Zhang, X.; et al. Cell-free microRNA expression signatures in urine serve as novel noninvasive biomarkers for diagnosis and recurrence prediction of bladder cancer. Oncotarget 2017, 8, 40832–40842. [Google Scholar] [CrossRef] [Green Version]
  99. Juracek, J.; Peltanova, B.; Dolezel, J.; Fedorko, M.; Pacik, D.; Radova, L.; Vesela, P.; Svoboda, M.; Slaby, O.; Stanik, M. Genome-wide identification of urinary cell-free microRNAs for noninvasive detection of bladder cancer. J. Cell Mol. Med. 2018, 22, 2033–2038. [Google Scholar] [CrossRef] [Green Version]
  100. Jiang, F.; Li, C.; Han, J.; Wang, L. Diagnostic Value of Combination of MicroRNA-192 in Urinary Sediment and B-Ultrasound for Bladder Cancer. Technol. Cancer Res. Treat. 2020, 19, 1533033819894573. [Google Scholar] [CrossRef] [Green Version]
  101. Matsuzaki, K.; Fujita, K.; Jingushi, K.; Kawashima, A.; Ujike, T.; Nagahara, A.; Ueda, Y.; Tanigawa, G.; Yoshioka, I.; Ueda, K.; et al. MiR-21-5p in urinary extracellular vesicles is a novel biomarker of urothelial carcinoma. Oncotarget 2017, 8, 24668–24678. [Google Scholar] [CrossRef] [Green Version]
  102. Wang, J.; Zhao, L.; Peng, X.; Liu, K.; Zhang, C.; Chen, X.; Han, Y.; Lai, Y. Evaluation of miR-130 family members as circulating biomarkers for the diagnosis of bladder cancer. J. Clin. Lab. Anal. 2020, 34, e23517. [Google Scholar] [CrossRef]
  103. Sapre, N.; Macintyre, G.; Clarkson, M.; Naeem, H.; Cmero, M.; Kowalczyk, A.; Anderson, P.D.; Costello, A.J.; Corcoran, N.M.; Hovens, C.M. A urinary microRNA signature can predict the presence of bladder urothelial carcinoma in patients undergoing surveillance. Br. J. Cancer 2016, 114, 454–462. [Google Scholar] [CrossRef]
  104. Usuba, W.; Urabe, F.; Yamamoto, Y.; Matsuzaki, J.; Sasaki, H.; Ichikawa, M.; Takizawa, S.; Aoki, Y.; Niida, S.; Kato, K.; et al. Circulating miRNA panels for specific and early detection in bladder cancer. Cancer Sci. 2019, 110, 408–419. [Google Scholar] [CrossRef] [PubMed]
  105. Lakshminarasimhan, R.; Liang, G. The Role of DNA Methylation in Cancer. Adv. Exp. Med. Biol. 2016, 945, 151–172. [Google Scholar] [PubMed]
  106. Witjes, J.A.; Morote, J.; Cornel, E.B.; Gakis, G.; van Valenberg, F.J.P.; Lozano, F.; Sternnberg, I.A.; Willemsen, E.; Hegemann, M.L.; Paitan, Y.; et al. Performance of the bladder EpiCheckTM methylation test for patients under surveillance for non−muscle-invasive bladder cancer: Results of a multicenter, prospective, blinded clinical trial. Eur. Urol. Oncol. 2018, 1, 307–313. [Google Scholar] [CrossRef] [PubMed]
  107. Andrea, D.D.; Soria, F.; Zehetmayer, S.; Gust, K.M.; Korn, S.; Witjes, J.A.; Shariat, S.F. Diagnostic accuracy, clinical utility and influence on decision making of a methylation urine biomarker test in the surveillance of non-muscle-invasive bladder cancer. BJU Int. 2019, 123, 959–967. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  108. Bosschieter, J.; Nieuwenhuijzen, J.A.; Hentschel, A.; van Splunter, A.P.; Segerink, L.I.; Vis, A.N.; Wilting, S.M.; Lissenberg-Witte, B.I.; van Moorrselaar, R.J.A.; Steenbergen, R.D. A two-gene methylation signature for the diagnosis of bladder cancer in urine. Epigenomics 2019, 11, 337–347. [Google Scholar] [CrossRef]
  109. Pierconti, F.; Martini, M.; Fiorentino, V.; Cenci, T.; Capodimeonti, S.; Straccia, P.; Sacco, E.; Pugliese, D.; Cindolo, L.; Larocca, L.M.; et al. The combination cytology/EpiCheck test in non muscle invasive bladder carcinoma follow-up: Effective tool or useless expence? Urol. Oncol. Semin. Orig. Investig. 2021, 39, 131. [Google Scholar]
  110. Wasserstrom, A.; Frumkin, D.; Dotan, Z.; Bukin, E.; Gadish, T.; Hanuka, S.; Knirsh, R.; Darawsha, A.E.; Leibovitch, I. Mp13-15 Molecular Urine Cytology–Bladder EpiCheck Is a Novel Molecular Diagnostic Tool for Monitoring of Bladder Cancer Patients. J. Urol. 2016, 195, e140. [Google Scholar] [CrossRef] [Green Version]
  111. Trenti, E.; D’Elia, C.; Mian, C.; Schwienbacher, C.; Hanspeter, E.; Pycha, A.; Kafka, A.; Degener, S.; Danuser, H.; Roth, S.; et al. Diagnostic predictive value of the Bladder EpiCheck test in the follow-up of patients with non-muscle-invasive bladder cancer. Cancer Cytopathol. 2019, 127, 465–469. [Google Scholar] [CrossRef]
  112. Van Kessel, K.; Van Neste, L.; Lurkin, I.; Zwarthoff, E.C.; Van Criekinge, W. Evaluation of an Epigenetic Profile for the Detection of Bladder Cancer in Patients with Hematuria. J. Urol. 2016, 195, 601–607. [Google Scholar] [CrossRef]
  113. Roperch, J.P.; Grandchamp, B.; Desgrandchamps, F.; Mongiat-Artus, P.; Ravery, V.; Ouzaid, I.; Roupret, M.; Phe, V.; Coifu, C.; Tubach, F.; et al. Promoter hypermethylation of HS3ST2, SEPTIN9 and SLIT2 combined with FGFR3 mutations as a sensitive/specific urinary assay for diagnosis and surveillance in patients with low or high-risk non-muscle-invasive bladder cancer. BMC Cancer 2016, 16, 704. [Google Scholar] [CrossRef]
  114. Wang, Y.; Yu, Y.; Ye, R.; Zhang, D.; Li, Q.; An, D.; Fang, L.; Lin, Y.; Hou, Y.; Xu, A.; et al. An epigenetic biomarker combination of PCDH17 and POU4F2 detects bladder cancer accurately by methylation analyses of urine sediment DNA in Han Chinese. Oncotarget 2016, 7, 2754–2764. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  115. Van Kessel, K.; Beukers, W.; Lurkin, I.; Ziel-van der Made, A.; vaan der Keur, K.; Boormans, J.L.; Dyrskjot, L.; Marquez, M.; Orntoft, T.F.; Real, F.X.; et al. Validation of a DNA Methylation-Mutation Urine Assay to Select Patients with Hematuria for Cystoscopy. J. Urol. 2017, 197 Pt 1, 590–595. [Google Scholar] [CrossRef] [PubMed]
  116. Steinbach, D.; Kaufmann, M.; Hippe, J.; Gajda, M.; Grimm, M. High Detection Rate for Non-Muscle-Invasive Bladder Cancer Using an Approved DNA Methylation Signature Test. Clin. Genitourin. Cancer 2020, 18, 210–221. [Google Scholar] [CrossRef] [PubMed]
  117. Pietrusinski, M.; Kepczynski, L.; Jedrzejczyk, A.; Borkowska, E.; Traczyk-Borszynska, M.; Costantinou, M.; Kaluzewski, B.; Borowiec, M. Detection of bladder cancer in urine sediments by a hypermethylation panel of selected tumor suppressor genes. Cancer Biomark. 2017, 18, 47–59. [Google Scholar] [CrossRef] [PubMed]
  118. Feber, A.; Dhami, P.; Dong, L.; de Winter, P.; Tan, W.S.; Martinez-Fernandez, M.; Paul, D.S.; Hynes-Allen, A.; Rezaee, S.; Gurung, P.; et al. UroMark-a urinary biomarker assay for the detection of bladder cancer. Clin. Epigenetics 2017, 9, 8. [Google Scholar] [CrossRef] [Green Version]
  119. Wei Shen, T.; Feber, A.; Dong, L.; Sarpong, R.; Rezaee, S.; Rodney, S.; Khetrapal, P.; de Winter, P.; Ocampo, F.; Jalil, R.; et al. DETECT I & DETECT II: A study protocol for a prospective multicentre observational study to validate the UroMark assay for the detection of bladder cancer from urinary cells. BMC Cancer 2017, 17, 767. [Google Scholar]
  120. Sanchez Calle, A.; Kawamura, Y.; Yamamoto, Y.; Takeshita, F.; Ochiya, T. Emerging roles of long non-coding RNA in cancer. Cancer Sci. 2018, 109, 2093–2100. [Google Scholar] [CrossRef]
  121. Sin, M.L.Y.; Mach, K.E.; Sinha, R.; Wu, F.; Trivedi, D.R.; Altobelli, E.; Jensen, K.C.; Sahoo, D.; Lu, Y.; Liao, J.C. Deep Sequencing of Urinary RNAs for Bladder Cancer Molecular Diagnostics. Clin. Cancer Res. 2017, 23, 3700–3710. [Google Scholar] [CrossRef] [Green Version]
  122. Zhang, S.; Du, L.; Wang, L.; Jiang, X.; Zhan, Y.; Li, J.; Yan, K.; Duan, W.; Zhao, Y.; Wang, L.; et al. Evaluation of serum exosomal LncRNA-based biomarker panel for diagnosis and recurrence prediction of bladder cancer. J. Cell Mol. Med. 2019, 23, 1396–1405. [Google Scholar] [CrossRef] [Green Version]
  123. Zhang, Y.; Lutao, D.; Wang, L.; Jiang, X.; Zhang, S.; Li, J.; Yan, K.; Duan, W.; Zhao, Y.; Wang, L.; et al. Expression signatures of exosomal long non-coding RNAs in urine serve as novel non-invasive biomarkers for diagnosis and recurrence prediction of bladder cancer. Mol. Cancer 2018, 17, 142. [Google Scholar] [CrossRef]
  124. Duan, W.; Lutao, D.; Jiang, X.; Wang, R.; Suzhen, Y.; Xie, Y.; Yan, K.; Wang, Q.; Wang, L.; Zhang, X.; et al. Identification of a serum circulating lncRNA panel for the diagnosis and recurrence prediction of bladder cancer. Oncotarget 2016, 7, 78850–78858. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  125. Du, L.; Duan, W.; Jiang, X.; Zhao, L.; Li, J.; Wang, R.; Yan, S.; Xie, Y.; Yan, K.; Wang, Q.; et al. Cell-free lncRNA expression signatures in urine serve as novel non-invasive biomarkers for diagnosis and recurrence prediction of bladder cancer. J. Cell Mol. Med. 2018, 22, 2838–2845. [Google Scholar] [CrossRef] [PubMed]
  126. Abbastabar, M.; Sarfi, M.; Golestani, A.; Karimi, A.; Pourmand, G.; Khalili, E. Tumor-derived urinary exosomal long non-coding RNAs as diagnostic biomarkers for bladder cancer. EXCLI J. 2020, 19, 301–310. [Google Scholar] [PubMed]
  127. Abdolmaleki, F.; Ghafoui-Fard, S.; Taheri, M.; Mordadi, A.; Afsharpad, M.; Varmazyar, S.; Nazparvar, B.; Oskooei, V.K.; Omrani, M.D. Expression analysis of a panel of long non-coding RNAs (lncRNAs) revealed their potential as diagnostic biomarkers in bladder cancer. Genomics 2020, 112, 677–682. [Google Scholar] [CrossRef] [PubMed]
  128. Yazarlou, F.; Modarressi, M.H.; Mowla, S.J.; Oskooei, V.K.; Motevaseli, E.; Tooli, L.F.; Nekoohesh, L.; Eghbali, M.; Ghafouri-Fard, S.; Afsharpad, M. Urinary exosomal expression of long non-coding RNAs as diagnostic marker in bladder cancer. Cancer Manag. Res. 2018, 10, 6357–6365. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  129. Wang, W.; Yin, Z. Diagnostic value of long non-coding RNA H19, UCA1, and HOTAIR as promising biomarkers in human bladder cancer. Int. J. Clin. Exp. Pathol. 2017, 10, 11659–11665. [Google Scholar]
  130. Ribal, M.J.; Mengual, L.; Lozano, J.J.; Ingelmo-Torres, M.; Palou, J.; Rodriguez-Faba, O.; Witjes, J.A.; Van der Heijden, A.; Medina, R.; Conde, J.M.; et al. Gene expression test for the non-invasive diagnosis of bladder cancer: A prospective, blinded, international and multicenter validation study. Eur. J. Cancer 2016, 54, 131. [Google Scholar] [CrossRef]
  131. Johnson, C.; Ivanisevic, J.; Siuzdak, G. Metabolomics: Beyond biomarkers and towards mechanisms. Nat. Rev. Mol. Cell Biol. 2016, 17, 451–459. [Google Scholar] [CrossRef] [Green Version]
  132. Griffin, J.L. Twenty years of metabonomics: So what has metabonomics done for toxicology? Xenobiotica 2020, 50, 110–114. [Google Scholar] [CrossRef]
  133. Gupta, A.; Bansal, N.; Mitash, N.; Kumar, D.; Kumar, M.; Sankwar, S.N.; Mandhani, A.; Singh, U.P. NMR-derived targeted serum metabolic biomarkers appraisal of bladder cancer: A pre- and post-operative evaluation. J. Pharm. Biomed. Anal. 2020, 183, 113134. [Google Scholar] [CrossRef]
  134. Huttanus, H.; Vu, T.; Guruli, G.; Tracey, A.; Carswell, W.; Said, N.; Du, P.; Parkinson, B.G.; Orlando, G.; Robertson, J.L.; et al. Raman chemometric urinalysis (Rametrix) as a screen for bladder cancer. PLoS ONE 2020, 15, e0237070. [Google Scholar] [CrossRef] [PubMed]
  135. Gok, S.; Aydin, O.Z.; Sural, Y.S.; Zorlu, F.; Bayol, U.; Severcan, F. Bladder cancer diagnosis from bladder wash by Fourier transform infrared spectroscopy as a novel test for tumor recurrence. J. Biophotonics 2016, 9, 967–975. [Google Scholar] [CrossRef] [PubMed]
  136. Chi-Hung, S.; Chen, C.; Lin, J.; Chen, C.; Fu, S.; Chen, Y.; Chang, Y.; Yu, J.; Tsui, K.; Juo, C.; et al. Metabolite marker discovery for the detection of bladder cancer by comparative metabolomics. Oncotarget 2017, 8, 38802–38810. [Google Scholar]
  137. Tan, G.; Wang, H.; Yuan, J.; Qin, W.; Dong, X.; Wu, H.; Meng, P. Three serum metabolite signatures for diagnosing low-grade and high-grade bladder cancer. Sci. Rep. 2017, 7, 46176. [Google Scholar] [CrossRef] [Green Version]
  138. Frantzi, M.; van Kessel, K.E.; Zwarthoff, E.C.; Marquez, M.; Rava, M.; Malats, N.; Merseburger, A.S.; Katafigiotis, I.; Stravodimos, K.; Mullen, W.; et al. Development and Validation of Urine-based Peptide Biomarker Panels for Detecting Bladder Cancer in a Multi-center Study. Clin. Cancer Res. 2016, 22, 4077–4086. [Google Scholar] [CrossRef] [Green Version]
  139. Kollarik, B.; Zvarik, M.; Bujdak, P.; Weibl, P.; Rybar, L.; Sikurova, L.; Hunakova, L. Urinary fluorescence analysis in diagnosis of bladder cancer. Neoplasma 2018, 65, 234–241. [Google Scholar] [CrossRef] [Green Version]
  140. Dapeng, D.; Chen, M.; Xiao, X.; Cao, P.; Li, S. Novel serum peptide model revealed by MALDI-TOF-MS and its diagnostic value in early bladder cancer. Int. J. Biol. Markers 2020, 35, 59–66. [Google Scholar]
  141. Monteiro-Reis, S.; Blanca, A.; Tedim-Moreira, J.; Carneiro, I.; Montezuma, D.; Monteiro, P.; Oliveira, J.; Antunes, L.; Henrique, R.; Lopez-Beltran, A.; et al. A Multiplex Test Assessing MiR663a(me) and VIMme in Urine Accurately Discriminates Bladder Cancer from Inflammatory Conditions. J. Clin. Med. 2020, 9, 605. [Google Scholar] [CrossRef] [Green Version]
  142. Fantony, J.J.; Longo, T.A.; Gopalakrishna, A.; Owusu, R.; Lance, R.S.; Foo, W.; Inman, B.A.; Abern, M.R. Urinary NID2 and TWIST1 methylation to augment conventional urine cytology for the detection of bladder cancer. Cancer Biomark 2017, 18, 381–387. [Google Scholar] [CrossRef] [Green Version]
  143. Van der Heijden, A.G.; Mengual, L.; Ingelmo-Torres, M.; Lozano, J.J.; Rijt-van de Westerlo, C.C.M.; Baixauli, M.; Geavlete, B.; Moldoveanud, C.; Ene, C.; Dinney, C.P.; et al. Urine cell-based DNA methylation classifier for monitoring bladder cancer. Clin. Epigenetics 2018, 10, 71. [Google Scholar] [CrossRef]
  144. Dahmcke, C.M.; Steven, K.E.; Larsen, L.K.; Poulsen, A.L.; Abdul-Al, A.; Dahl, C.; Guldberg, P. A Prospective Blinded Evaluation of Urine-DNA Testing for Detection of Urothelial Bladder Carcinoma in Patients with Gross Hematuria. Eur. Urol. 2016, 70, 916–919. [Google Scholar] [CrossRef] [PubMed]
  145. Huang, H.; Du, J.; Jin, B.; Pang, L.; Duan, N.; Huang, C.; Hou, J.; Yu, W.; Hao, H.; Li, H. Combination of Urine Exosomal mRNAs and lncRNAs as Novel Diagnostic Biomarkers for Bladder Cancer. Front. Oncol. 2021, 11, 667212. [Google Scholar] [CrossRef] [PubMed]
  146. Elsawy, A.; Awadalla, A.; Maher, S.; Ahmed, A.E.; Youssef, M.M.; Abol-Enein, H. Diagnostic Performance of Novel Urine-Based mRNA Tests (Xpert and Urinary Metabolomics Markers Assay) for Bladder Cancer Detection in Patients with Hematuria. Bladder Cancer 2020, 6, 319–328. [Google Scholar] [CrossRef]
  147. Fouad, H.; Salem, H.; El-Sayed Ellakwa, D.; Abdel-Hamid, M. MMP-2 and MMP-9 as prognostic markers for the early detection of urinary bladder cancer. J. Biochem. Mol. Toxicol. 2019, 33, e22275. [Google Scholar] [CrossRef]
  148. Courtade-Saidi, M.; Aziza, J.; d’Aure, D.; Berard, E.; Evrad, S.; Basset, C.; Lacoste-Collin, L. Immunocytochemical staining for p53 and Ki-67 helps to characterize urothelial cells in urine cytology. Cytopathology 2016, 27, 456–464. [Google Scholar] [CrossRef]
  149. Lenos, M.; Katafigiotis, I.; Vrettou, K.; Papaioannou, D.; Malta, F.; Trigka, E.A.; Sousouris, S.; Costantinides, C.; Mikou, P. Evaluation of double immunocytochemical staining for CK20 and P53 as a potential adjunct to cytology for urothelial cancer diagnosis. Cytopathology 2017, 28, 96–102. [Google Scholar] [CrossRef]
  150. Neal, D.; Amin, M.B.; Smith, S.C. CK20 versus AMACR and p53 immunostains in evaluation of Urothelial Carcinoma in Situ and Reactive Atypia. Diagn. Pathol. 2020, 15, 61. [Google Scholar] [CrossRef]
  151. Piaton, E.; Advenier, A.; Carre, C.; Decaussin-Petrucci, M.; Mege-Lechvallier, F.; Hutin, K.; Nennig, C.; Colombel, M.; Ruffion, A. p16/Ki-67 dual labeling and urinary cytology results according to the New Paris System for Reporting Urinary Cytology: Impact of extended follow-up. Cancer Cytopathol. 2017, 125, 552–562. [Google Scholar] [CrossRef] [Green Version]
  152. Nguyen, J.; Przybycin, C.G.; McKenney, J.; Magi-Galluzzi, C. Immunohistochemical staining patterns of Ki-67 and p53 in florid reactive urothelial atypia and urothelial carcinoma in situ demonstrate significant overlap. Hum. Pathol. 2020, 98, 81–88. [Google Scholar] [CrossRef]
  153. Brisuda, A.; Hacek, J.; Cechova, M.; Skapa, P.; Babjuk, M. Diagnosis of urinary bladder urothelial carcinoma by immunocytology with p53, MCM5, MCM2 and Ki-67 antibodies using cell blocks derived from urine. Cytopathology 2019, 30, 510–518. [Google Scholar] [CrossRef]
  154. Yikilmaz, T.N.; Dirim, A.; Ayva, E.S.; Ozdemir, H.; Ozkardes, H. Clinical Use of Tumor Markers for the Detection and Prognosis of Bladder Carcinoma: A Comparison of CD44, Cytokeratin 20 and Survivin. Urol. J. 2016, 13, 2677–2683. [Google Scholar] [PubMed]
  155. Thakur, B.; Kishore, S.; Dutta, K.; Kaushik, S.; Bhardwaj, A. Role of p53 and Ki-67 immunomarkers in carcinoma of urinary bladder. Indian J. Pathol. Microbiol. 2017, 60, 505–509. [Google Scholar] [CrossRef] [PubMed]
  156. Arias Stella, J.A., 3rd; Shah, A.B.; Gupta, N.S.; Williamson, S.R. CK20 and p53 Immunohistochemical Staining Patterns in Urinary Bladder Specimens with Equivocal Atypia. Arch. Pathol. Lab. Med. 2018, 142, 64–69. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  157. Pham, H.V.; Pantanowitz, L.; Liu, Y. Quantitative phase imaging to improve the diagnostic accuracy of urine cytology. Cancer Cytopathol. 2016, 124, 641–650. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  158. Dugas, S.; Muller, D.C.; Le Magnen, C.; Federe-Gsponer, J.; Seifert, H.; Ruiz, C.; Savic Prince, S.; Vlajnic, T.; Zellweger, T.; Mertz, K.D.; et al. Immunocytochemistry for ARID1A as a potential biomarker in urine cytology of bladder cancer. Cancer Cytopathol. 2019, 127, 578–585. [Google Scholar] [CrossRef]
  159. Daneshmand, S.; Bazargani, S.T.; Bivalacqua, T.J.; Holzbeierlein, J.M.; Willard, B.; Taylor, J.M.; Liao, J.C.; Pohar, K.; Tierney, J.; Konety, B. Blue light cystoscopy for the diagnosis of bladder cancer: Results from the US prospective multicenter registry. Urol. Oncol. Semin. Orig. Investig. 2018, 36, 361.e1–361.e6. [Google Scholar] [CrossRef]
  160. Mantica, G.; Simonato, A.; Du Plessis, D.E.; Maffezzini, M.; De Rose, A.F.; van der Merwe, A.; Terrone, C. The pathologist’s role in the detection of rare variants of bladder cancer and analysis of the impact on incidence and type detection. Minerva. Urol. Nefrol. 2018, 70, 594–597. [Google Scholar] [CrossRef]
  161. Mantica, G.; Tappero, S.; Parodi, S.; Piol, N.; Spina, B.; Malinaric, R.; Balzarini, F.; Borghesi, M.; van der Merwe, A.; Suardi, N.; et al. Bladder cancer histological variants: Which parameters could predict the concordance between transurethral resection of bladder tumor and radical cystectomy specimens? Cent European J. Urol. 2021, 74, 355–361. [Google Scholar] [CrossRef]
  162. Meeks, J.J.; Al-Ahmadie, H.; Faltas, B.M.; Taylor, J.A., 3rd; Flaig, T.W.; DeGraff, D.J.; Christensen, E.; Woolbright, B.L.; McConkey, D.J.; Dyrskjot, L. Genomic heterogeneity in bladder cancer: Challenges and possible solutions to improve outcomes. Nat. Rev. Urol. 2020, 17, 259–270. [Google Scholar] [CrossRef]
Ijerph 19 09648 g001 550
Figure 1. PRISMA flowchart.
Figure 1. PRISMA flowchart.
Ijerph 19 09648 g001
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Malinaric, R.; Mantica, G.; Lo Monaco, L.; Mariano, F.; Leonardi, R.; Simonato, A.; Van der Merwe, A.; Terrone, C. The Role of Novel Bladder Cancer Diagnostic and Surveillance Biomarkers—What Should a Urologist Really Know? Int. J. Environ. Res. Public Health 2022, 19, 9648. https://doi.org/10.3390/ijerph19159648

AMA Style

Malinaric R, Mantica G, Lo Monaco L, Mariano F, Leonardi R, Simonato A, Van der Merwe A, Terrone C. The Role of Novel Bladder Cancer Diagnostic and Surveillance Biomarkers—What Should a Urologist Really Know? International Journal of Environmental Research and Public Health. 2022; 19(15):9648. https://doi.org/10.3390/ijerph19159648

Chicago/Turabian Style

Malinaric, Rafaela, Guglielmo Mantica, Lorenzo Lo Monaco, Federico Mariano, Rosario Leonardi, Alchiede Simonato, André Van der Merwe, and Carlo Terrone. 2022. "The Role of Novel Bladder Cancer Diagnostic and Surveillance Biomarkers—What Should a Urologist Really Know?" International Journal of Environmental Research and Public Health 19, no. 15: 9648. https://doi.org/10.3390/ijerph19159648

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop