Diagnostic accuracy of NMP 22 and urine cytology for detection of transitional cell carcinoma urinary bladder taking cystoscopy as gold standard

Unleashing the power of urine‑based biomarkers in diagnosis, prognosis and monitoring of bladder cancer (Review)

Bladder cancer (BCa) is a prevalent malignant neoplasm of the urinary tract with high incidence rate, frequent recurrence and rapid disease progression. Conventional approaches for diagnosing, prognosticating and monitoring BCa often rely on invasive procedures such as cystoscopy and tissue biopsy, which are associated with high costs and low patient compliance for follow‑up. Liquid biopsies have advantages, such as being non‑invasive, real‑time, and reproducible, in obtaining diverse biomarkers derived from cellular, molecular, proteomic and genetic signatures in urine or plasma samples. Although plasma‑based biomarkers have been clinically validated, urine provides greater specificity for directly assessing biological materials from urological sources. The present review summarizes advancements and current limitations in urinary protein, genetic and epigenetic biomarkers for disease progression and treatment response of BC, compares performance and application scenarios of urine and blood biomarkers and explores how urinary biomarkers may serve as an alternative or complementary tool to traditional diagnostic methods. The integration of urine‑based or plasma‑based biomarkers into existing diagnostic workflows offers promising avenues for improving accuracy and efficiency of diagnosis in the management of BCa. Notably, the emergence of synthetic biomarkers and urine metabolites, combined with artificial intelligence or bioinformatic technologies, has promise in the screening of potential targets. Continued research and validation efforts are needed to translate these findings into routine clinical practice, ultimately improving patient outcomes and decreasing the burden of BCa.

Stratifying risk of disease in haematuria patients using machine learning techniques to improve diagnostics

Background

Detailed and invasive clinical investigations are required to identify the causes of haematuria. Highly unbalanced patient population (predominantly male) and a wide range of potential causes make the ability to correctly classify patients and identify patient-specific biomarkers a major challenge. Studies have shown that it is possible to improve the diagnosis using multi-marker analysis, even in unbalanced datasets, by applying advanced analytical methods. Here, we applied several machine learning algorithms to classify patients from the haematuria patient cohort (HaBio) by analysing multiple biomarkers and to identify the most relevant ones.

Materials and methods

We applied several classification and feature selection methods (k-means clustering, decision trees, random forest with LIME explainer and CACTUS algorithm) to stratify patients into two groups: healthy (with no clear cause of haematuria) or sick (with an identified cause of haematuria e.g., bladder cancer, or infection). The classification performance of the models was compared. Biomarkers identified as important by the algorithms were also analysed in relation to their involvement in the pathological processes.

Results

Results showed that a high unbalance in the datasets significantly affected the classification by random forest and decision trees, leading to the overestimation of the sick class and low model performance. CACTUS algorithm was more robust to the unbalance in the dataset. CACTUS obtained a balanced accuracy of 0.747 for both genders, 0.718 for females and 0.803 for males. The analysis showed that in the classification process for the whole dataset: microalbumin, male gender, and tPSA emerged as the most informative biomarkers. For males: age, microalbumin, tPSA, cystatin C, BTA, HAD and S100A4 were the most significant biomarkers while for females microalbumin, IL-8, pERK, and CXCL16.

Conclusions

CACTUS algorithm demonstrated improved performance compared with other methods such as decision trees and random forest. Additionally, we identified the most relevant biomarkers for the specific patient group, which could be considered in the future as novel biomarkers for diagnosis. Our results have the potential to inform future research and provide new personalised diagnostic approaches tailored directly to the needs of the individuals.

Changes in Urinary Exosomal Nuclear Matrix Protein-22 in Dogs With Urothelial Carcinoma: A Pilot Study

BACKGROUND/AIM

Nuclear matrix protein-22 (NMP-22) is widely used in human medicine as a prognostic and diagnostic tool for urothelial carcinoma (UC). In addition, the use of urinary exosomes as a liquid biopsy tool is emerging for the diagnosis of certain types of cancer in human medicine. This study aimed to investigate the change in urinary exosomal NMP-22 for the diagnosis of UC in dogs.

PATIENTS AND METHODS

Among canine patients who visited the veterinary hospital, urine was collected from those whose owners provided consent. A total of 23 dogs (UC group, n=6; control group, n=17) were included in the analysis. After exosomes were isolated from the urine, NMP-22 was measured using enzyme-linked immunosorbent assay.

RESULTS

In the UC group, the expression of NMP-22 in urinary exosomes was significantly higher than that in non-UC groups (p<0.0001).

CONCLUSION

NMP-22 is significantly increased in exosomes in the urine of dogs diagnosed with UC, suggesting that urinary exosome NMP-22 can be considered as one of the liquid biopsy tools for diagnosing UC in dogs.

Diagnostic Performance of Biomarkers for Bladder Cancer Detection Suitable for Community and Primary Care Settings: A Systematic Review and Meta-Analysis

Evidence on the use of biomarkers to detect bladder cancer in the general population is scarce. This study aimed to systematically review evidence on the diagnostic performance of biomarkers which might be suitable for use in community and primary care settings [PROSPERO Registration: CRD42021258754]. Database searches on MEDLINE and EMBASE from January 2000 to May 2022 resulted in 4914 unique citations, 44 of which met inclusion criteria. Included studies reported on 112 biomarkers and combinations. Heterogeneity of designs, populations and outcomes allowed for the meta-analysis of three biomarkers identified in at least five studies (NMP-22, UroVysion, uCyt+). These three biomarkers showed similar discriminative ability (adjusted AUC estimates ranging from 0.650 to 0.707), although for NMP-22 and UroVysion there was significant unexplained heterogeneity between included studies. Narrative synthesis revealed the potential of these biomarkers for use in the general population based on their reported clinical utility, including effects on clinicians, patients, and the healthcare system. Finally, we identified some promising novel biomarkers and biomarker combinations (N < 3 studies for each biomarker/combination) with negative predictive values of ≥90%. These biomarkers have potential for use as a triage tool in community and primary care settings for reducing unnecessary specialist referrals. Despite promising emerging evidence, further validation studies in the general population are required at different stages within the diagnostic pathway.

Biomarkers to assess the risk of bladder cancer in patients presenting with haematuria are gender-specific

Introduction

Haematuria is a common red flag symptom of urinary tract cancer. Bladder cancer (BC) is the most common cancer to present with haematuria. Women presenting with haematuria are often underdiagnosed. Currently, no gender-specific tests are utilized in clinical practice. Considerable healthcare resources are needed to investigate causes of haematuria and this study was set up to help identify markers of BC. The aim of the study was to define biomarker algorithms in haematuria patients using an expanded panel of biomarkers to diagnose BC and investigate if the algorithms are gender-specific.

Materials and Methods

A total of n=675 patients with a history of haematuria were recruited from Northern Ireland hospitals. Patients were collected on a 2:1 ratio, non-BC (control) n=474: BC n=201. A detailed clinical history, urine and blood samples were collected. Biomarkers, known to be involved in the pathobiology underlying bladder carcinogenesis were investigated. Biomarkers differentially expressed between groups were investigated using Wilcoxon rank sum and linear regression.

Results

Biomarkers were gender specific. Two biomarker-algorithms were identified to triage haematuria patients; male - u_NSE, s_PAI-1/tPA, u_midkine, u_NGAL, u_MMP-9/TIMP-1 and s_prolactin (u=urine; s=serum); sensitivity 71.8%, specificity 72.8%; AUROC 0.795; and female urine biomarkers - IL-12p70, IL-13, midkine and clusterin; sensitivity 83.7%, specificity 79.7%; AUROC 0.865. Addition of the clinical variable infection to both algorithms increased both AUROC to 0.822 (DeLong p=0.014) and to 0.923 (DeLong p=0.004) for males and females, respectively. Combining clinical risk factors with biomarker algorithms would enable application of the algorithms to triage haematuria patients.

Conclusion

Using gender-specific biomarker algorithms in combination with clinical risks that are associated with BC would allow clinicians to better manage haematuria patients and potentially reduce underdiagnosis in females. In this study, we demonstrate, for the first time, that blood and urine biomarkers are gender-specific when assessing risk of BC in patients who present with blood in their urine. Combining biomarker data with clinical factors could improve triage when referring patients for further investigations.

Diagnostic performance of nuclear matrix protein 22 and urine cytology for bladder cancer: A meta-analysis

PURPOSE

To compare and analyze the diagnostic efficacy of nuclear matrix protein 22 (NMP22) and urine cytology (UC) in the diagnosis of bladder cancer.

METHODS

Search the Chinese and English studies on NMP22 and urinary cytology in the diagnosis of bladder tumors published between 1999 and June, and conduct quality evaluation, data extraction and analysis.

RESULTS

A total of 397 related articles were retrieved, and 12 articles were finally included after screening, including 2456 subjects. The heterogeneity test shows that there is no discernible threshold effect. Perform meta-analysis according to the random effects model. The results showed that the total sensitivity of NMP22 and UC were 0.79 (95% CI [0.73, 0.84]) (CI: Confidence interval), 0.55 (95% CI [0.41, 0.69]), and the total specificity 0.59 (95% CI [0.46], respectively, 0.71), 0.91 (95% CI (0.81, 0.96]), +LR 1.9 (95% CI [1.4, 2.6]) (+LR: positive likelihood ration), 5.9 (95% CI [3.3, 10.6]), -LR 0.35 (-LR: negative likelihood ration), respectively (95% CI [0.27, 0.47]), 0.49 (95% CI [0.38, 0.64]), diagnostic odds ratios 5 (95% CI [3, 9]), 12 (95% CI [7, 21]). The area under the summary receiver operating characteristics curve (AUC) was 0.79 (95% CI [0.75, 0.82]) and 0.81 (95% CI [0.77, 0.84]), respectively.

CONCLUSIONS

NMP22 has moderate diagnostic efficiency for bladder cancer. Its sensitivity is greater than UC, but its specificity is significantly lower than that of UC. At present, it cannot replace traditional cystoscopy and UC, but it can be combined to detect bladder tumors. It plays a major role in screening, postoperative monitoring and follow-up.

Diagnostic Value of Telomerase Activity in Patients With Bladder Cancer: A Meta-Analysis of Diagnostic Test

Background

This study aimed to evaluate the diagnostic value of telomerase activity (TA) for bladder cancer (BC) by meta-analysis.

Methods

We conducted a systematic search of studies published on PubMed, Embase, and Web of Science up to June 1, 2019. We used Stata 15 and Review Manager 5.3 for calculations and statistical analysis.

Results

To evaluate the diagnostic value of TA for BC, we performed a meta-analysis on 22 studies, with a total of 2,867 individuals, including sensitivity, specificity, positive and negative likelihood ratio (PLR, NLR), diagnostic odds ratio (DOR), and 95% confidence intervals (CIs). The pooled parameters were calculated from all studies, and we found a sensitivity of 0.79 (95% CI: 0.72-0.84), a specificity of 0.91 (95% CI: 0.87-0.94), a PLR of 8.91 (95% CI: 5.91-13.43), an NLR of 0.24 (95% CI: 0.15-0.37), a DOR of 37.90 (95% CI: 23.32-61.59), and an AUC of 0.92 (95% CI: 0.90-0.94). We also conducted a subgroup analysis based on the different stages and grades of BC. Results from the subgroup analysis showed that there was no significant difference in TA in either high and low stages of BC, but that low-grade tumors had a lower TA than high-grade tumours.

Conclusions

TA can be used as a potential biomarker for the diagnosis of bladder cancer with its high specificity. Rigorous and high-quality prospective studies are required to verify our conclusion.