Identification of novel snoRNA-based biomarkers for clear cell renal cell carcinoma from urine-derived extracellular vesicles.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of RCC with high rates of metastasis. Targeted therapies such as tyrosine kinase and checkpoint inhibitors have improved treatment success, but therapy-related side effects and tumor recurrence remain a challenge. As a result, ccRCC still have a high mortality rate. Early detection before metastasis has great potential to improve outcomes, but no suitable biomarker specific for ccRCC is available so far. Therefore, molecular biomarkers derived from body fluids have been investigated over the past decade. Among them, RNAs from urine-derived extracellular vesicles (EVs) are very promising. METHODS: RNA was extracted from urine-derived EVs from a cohort of 78 subjects (54 ccRCC patients, 24 urolithiasis controls). RNA-seq was performed on the discovery cohort, a subset of the whole cohort (47 ccRCC, 16 urolithiasis). Reads were then mapped to the genome, and expression was quantified based on 100 nt long contiguous genomic regions. Cluster analysis and differential region expression analysis were performed with adjustment for age and gender. The candidate biomarkers were validated by qPCR in the entire cohort. Receiver operating characteristic, area under the curve and odds ratios were used to evaluate the diagnostic potential of the models. RESULTS: An initial cluster analysis of RNA-seq expression data showed separation by the subjects' gender, but not by tumor status. Therefore, the following analyses were done, adjusting for gender and age. The regions differentially expressed between ccRCC and urolithiasis patients mainly overlapped with small nucleolar RNAs (snoRNAs). The differential expression of four snoRNAs (SNORD99, SNORD22, SNORD26, SNORA50C) was validated by quantitative PCR. Confounder-adjusted regression models were then used to classify the validation cohort into ccRCC and tumor-free subjects. Corresponding accuracies ranged from 0.654 to 0.744. Models combining multiple genes and the risk factors obesity and hypertension showed improved diagnostic performance with an accuracy of up to 0.811 for SNORD99 and SNORA50C (p = 0.0091). CONCLUSIONS: Our study uncovered four previously unrecognized snoRNA biomarkers from urine-derived EVs, advancing the search for a robust, easy-to-use ccRCC screening method.

Comprehensive Prostate Fluid-Based Spectral Libraries for Enhanced Protein Detection in Urine.

Biofluids contain molecules in circulation and from nearby organs that can be indicative of disease states. Characterizing the proteome of biofluids with DIA-MS is an emerging area of interest for biomarker discovery; yet, there is limited consensus on DIA-MS data analysis approaches for analyzing large numbers of biofluids. To evaluate various DIA-MS workflows, we collected urine from a clinically heterogeneous cohort of prostate cancer patients and acquired data in DDA and DIA scan modes. We then searched the DIA data against urine spectral libraries generated using common library generation approaches or a library-free method. We show that DIA-MS doubles the sample throughput compared to standard DDA-MS with minimal losses to peptide detection. We further demonstrate that using a sample-specific spectral library generated from individual urines maximizes peptide detection compared to a library-free approach, a pan-human library, or libraries generated from pooled, fractionated urines. Adding urine subproteomes, such as the urinary extracellular vesicular proteome, to the urine spectral library further improves the detection of prostate proteins in unfractionated urine. Altogether, we present an optimized DIA-MS workflow and provide several high-quality, comprehensive prostate cancer urine spectral libraries that can streamline future biomarker discovery studies of prostate cancer using DIA-MS.

Analysis of the relationship between MYCN gene and serum NSE and urinary VMA levels and neuroblastoma pathological features and prognosis.

The purpose of this study was to explore the relationship between the MYCN gene, serum neuron-specific enolase (NSE), urinary vanillylmandelic acid (VMA) levels, and neuroblastoma pathological features and prognosis. Ninety-four children with neuroblastoma treated in the hospital were selected to compare the differences in MYCN gene amplification, serum NSE, and urinary VMA levels in children with different clinicopathological features and prognoses. The proportion of children with MYCN gene copy number ≥10 in INSS stage 3-4 was higher than that of children with INSS stage 1-2 (P < 0.05); the proportion of children with MYCN gene copy number ≥10 in high-risk children in the COG risk stratification was higher than that of children with intermediate and low risk (P < 0.05); the serum NSE of children aged >12 months higher than that of children aged ≤12 months (P < 0.05); serum NSE of children with tumors >500 cm3 higher than that of children with tumors ≤500 cm3 (P < 0.05); serum NSE and urinary VMA of children with INSS staging of stages 3-4 were higher than that of children with stages 1 to 2 (P < 0.05); serum NSE and urinary VMA in children with lymph node metastasis were higher than that of children without lymph node metastasis (P < 0.05); serum NSE of children with MYCN gene copy number ≥10 was higher than that of children without lymph node metastasis (P < 0.05); the proportion of children with MYCN gene copy number ≥10 who died, and the percentages of serum NSE and urinary VMA were higher than those of the surviving children (P < 0.05). MYCN gene amplification and serum NSE and urinary VMA levels were related to the age, tumor size, INSS stage, COG stage, lymph node metastasis, and prognosis of the children with neuroblastoma.

Alix-normalized exosomal programmed death-ligand 1 analysis in urine enables precision monitoring of urothelial cancer.

Anti-programmed death-ligand 1 (PD-L1) Ab-based therapies have demonstrated potential for treating metastatic urothelial cancer with high PD-L1 expression. Urinary exosomes are promising biomarkers for liquid biopsy, but urine's high variability requires normalization for accurate analysis. This study proposes using the PD-L1/Alix ratio to normalize exosomal PD-L1 signal intensity with Alix, an internal exosomal protein less susceptible to heterogeneity concerns than surface protein markers. Extracellular vesicles were isolated using ExoDisc and characterized using various methods, including ExoView to analyze tetraspanins, PD-L1, and Alix on individual exosomes. On-disc ELISA was used to evaluate PD-L1 and Alix-normalized PD-L1 in 15 urothelial cancer patients during the initial treatment cycle with Tecentriq. Results showed that Alix signal range was relatively uniform, whereas tetraspanin marker intensity varied for individual exosome particles. On-disc ELISA was more reliable for detecting exosomal PD-L1 expression than standard plate ELISA-based measurement. Using exosomal Alix expression for normalization is a more reliable approach than conventional methods for monitoring patient status. Overall, the study provides a practical and reliable method for detecting exosomal PD-L1 in urine samples from patients with urothelial cancer.

Prospective evaluation of the RT-PCR based urinary marker Bladder Epicheck® as a diagnostic tool in upper urinary tract tumor.

BACKGROUND: Upper-tract-urothelial-carcinoma (UTUC) represents 5-10% of all urothelial-neoplasms with increasing incidence in the last decades. Current standard tools for diagnosis of UTUC include cytology, computed tomography (CT) urography and ureterorenoscopy (URS). The aim of this study was to evaluate the impact of Bladder Epicheck® Test as diagnostic tool for UTUC diagnosis and recurrence. METHODS: Overall, 136 urine samples, selective collected from upper-urinary-tract before URS for suspicion of UTUC were analyzed with cytology and Bladder Epicheck® Test. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of both markers were calculated and compared to URS and/or histology as reference. RESULTS: UTUC was detected in 40 cases (33.3%), among them 30 were classified as low-grade (LG) and 10 as high-grade (HG). Overall sensitivity of Bladder Epicheck® for UTUC detection was 65% compared to 42.5% for cytology, increasing to 100% for Bladder Epicheck® and 90% for cytology if considering only HG tumors. Overall specificity of Bladder Epicheck® was 81.2% and of cytology 93.7%. PPV and NPV were 63.4% and 82.2% for Bladder Epicheck® and 77.2% and 76.5% for cytology. Considering an EpiScore cut-off >75, instead of 60, specificity of Bladder Epicheck® improves to 89% and PPV to 74.2%. Limitations include the use of a marker validated only for bladder-cancer and the relatively small number of cases. CONCLUSIONS: Due to its high sensitivity for HG tumors, the Bladder Epicheck® Test can be used in diagnosis and treatment decision-making of UTUC. Furthermore, it could be very useful in follow-up of UTUC, after endoscopic treatment to postpone or avoid unnecessary endoscopic exploration. Even if further studies are needed to validate these findings, Bladder Epicheck® could be a promising clinical tool for detection of UTUC.

Universal nanocomposite coating with antifouling and redox capabilities for electrochemical affinity biosensing in complex biological fluids.

Electrochemical affinity biosensors have the potential to facilitate the development of multiplexed point-of-care diagnostics in complex biological fluids. However, their commercial viability has been hindered by challenges such as electrode biofouling and the lack of inherent redox properties. To address this unmet need, we have developed a universal nanocomposite coating which is unique in its ability to not only allow oriented conjugation of the biorecognition element but also specific detection directly in complex biological fluids like serum and urine owing to its built-in antifouling and redox capabilities, thus improving suitability for point of care testing. This multifunctional coating comprises a 3D porous crosslinked bovine serum albumin matrix for oriented conjugation and antifouling properties with embedded graphene nanosheets modified with amino-ferrocene for enhanced conductivity and mediator-free biosensing. The coating showed minimal signal degradation despite prolonged exposure to 1% bovine serum albumin, artificial urine and untreated human serum for up to 30 days. To demonstrate its utility, we fabricated and tested proof-of-concept electrochemical immunosensors for bladder cancer protein biomarkers, specifically interleukin-8 (IL-8) and vascular endothelial growth factor (VEGF). The practical feasibility was highlighted by the excellent sensitivity and specificity observed for IL-8 and VEGF with a limit of detection of 41 pg mL-1 and 67 pg mL-1, respectively. Consequently, this universal nanocomposite-based electrochemical biosensing platform can be extended to the point of care testing of a broad spectrum of biomarkers present in complex biological fluids, thus enabling reliable and early diagnostics.

Enhancing breast cancer screening with urinary biomarkers and Random Forest supervised classification: A comprehensive investigation.

OBJECTIVES: Urinary sex hormones are investigated as potential biomarkers for the early detection of breast cancer, aiming to evaluate their relevance and applicability, in combination with supervised machine-learning data analysis, toward the ultimate goal of extensive screening. METHODS: Sex hormones were determined on urine samples collected from 250 post-menopausal women (65 healthy - 185 with breast cancer, recruited among the clinical patients of Candiolo Cancer Institute FPO-IRCCS (Torino, Italy). Two analytical procedures based on UHPLC-MS/HRMS were developed and comprehensively validated to quantify 20 free and conjugated sex hormones from urine samples. The quantitative data were processed by seven machine learning algorithms. The efficiency of the resulting models was compared. RESULTS: Among the tested models aimed to relate urinary estrogen and androgen levels and the occurrence of breast cancer, Random Forest (RF) proved to underscore all the other supervised classification approaches, including Partial Least Squares - Discriminant Analysis (PLS-DA), in terms of effectiveness and robustness. The final optimized model built on only five biomarkers (testosterone-sulphate, alpha-estradiol, 4-methoxyestradiol, DHEA-sulphate, and epitestosterone-sulphate) achieved an approximate 98% diagnostic accuracy on replicated validation sets. To balance the less-represented population of healthy women, a Synthetic Minority Oversampling TEchnique (SMOTE) data oversampling approach was applied. CONCLUSIONS: By means of tunable hyperparameters optimization, the RF algorithm showed great potential for early breast cancer detection, as it provides clear biomarkers ranking and their relative efficiency, allowing to ground the final diagnostic model on a restricted selection five steroid biomarkers only, as desirable for noninvasive tests with wide screening purposes.

Evaluation of URO17® to improve non-invasive detection of bladder cancer.

BACKGROUND: The gold standard for detecting bladder cancer is cystoscopy with biopsy or transurethral resection confirming histologic diagnosis. URO17® employs a chromogenically labeled monoclonal antibody to keratin 17 (k17), an intermediate filament cytoskeleton molecule associated with bladder, pancreatic, and cervical cancers. Preliminary studies evaluating k17 demonstrated a high sensitivity and specificity for the detection of bladder cancer, supporting the need for further study. OBJECTIVE: To evaluate the sensitivity and specificity of URO17. METHODS: This is a cross-sectional study of participants undergoing urologic procedures between July 6, 2018 and July 17, 2019 at a single institution. Patients undergoing cystectomy, endoscopic bladder and/or upper tract procedure for probable urothelial carcinoma comprised cases; patients undergoing urologic procedures for other reasons comprised the control group (i.e. prostatectomy, nephrectomy, etc.). Voided urine samples were at the time of procedure; a minority of participants underwent multiple resections in the study period, thus, as many as three urine samples were taken from any given participant. Samples were distributed for blinded testing with URO17. Sensitivity and specificity were calculated. RESULTS: In 152 participants and 167 samples, URO17 demonstrated an overall sensitivity of 90% and 92% and a specificity of 88% and 87%, respectively. In 76 participants and 91 samples from patients with suspected urothelial carcinoma, the sensitivity was 90% and 92%, and the specificity was 50% and 54%, respectively. No controls demonstrated a positive URO17 result, and URO17 superseded urine cytology detection of low-grade and high-grade Ta. False positive results were associated with inflamed tissue or urothelial atypia on histology; the large majority had a history of intravesical therapy. CONCLUSION: Limitations include cross-sectional design and convenience sampling. URO17 may improve sensitivity of urine cytology in the detection of urothelial cancer, though further study is required to refine the application of this biomarker in clinical practice.

Fragmentation patterns of cell-free DNA and somatic mutations in the urine of metastatic breast cancer patients.

BACKGROUND: Urinary cell-free deoxyribonucleic acid (DNA) (ucfDNA) holds promise as a biomarker; however, its potential remains largely unexplored. We examined the fragmentation pattern of ucfDNA and identified somatic mutations within urine samples from metastatic breast cancer (MBC) patients. METHODS: Urine and blood specimens were collected before treatment from 45 MBC patients and posttreatment urine samples from 16 of the 45 patients at the China National Cancer Center. Somatic mutations and tumor mutational burden (TMB) in the urine and plasma of 10 patients were analyzed by next-generation sequencing (NGS). Fragmentation patterns of cfDNA were displayed using electropherograms. Differences in the extracted amount of cfDNA, length of cfDNA fragments, and TMB between urine and plasma were compared using a Wilcoxon test. RESULTS: The fragmentation patterns of ucfDNA were categorized as follows: (1) profile A (n = 26) containing a short peak (100-200 bp) and a long peak (>1500 bp); (2) profile B (n = 8) containing only a long peak; and (3) profile C (n = 11) containing flat pattern. For profile A patients, the short-peaked ucfDNA circulating in the bloodstream was much shorter compared with plasma cfDNA (149 vs. 171 bp, Wilcoxon test, P = 0.023). The fragmentation patterns in lung metastasis patients exhibited a higher propensity toward profile C ( P = 0.002). After treatment, 87.5% of the patients exhibited consistent fragmentation patterns. The concordance rate for somatic mutations in the plasma and urine was 30%, and the median TMB of urine and plasma was not significantly different. CONCLUSIONS: This study established a fragmentation pattern for ucfDNA and detected somatic mutations in the urine of MBC patients. These results suggest the potential application of ucfDNA as a biomarker for MBC.

Scoring system for diagnosis and pretreatment risk assessment of neuroblastoma using urinary biomarker combinations.

The urinary catecholamine metabolites, homovanillic acid (HVA) and vanillylmandelic acid (VMA), are used for the adjunctive diagnosis of neuroblastomas. We aimed to develop a scoring system for the diagnosis and pretreatment risk assessment of neuroblastoma, incorporating age and other urinary catecholamine metabolite combinations. Urine samples from 227 controls (227 samples) and 68 patients with neuroblastoma (228 samples) were evaluated. First, the catecholamine metabolites vanillactic acid (VLA) and 3-methoxytyramine sulfate (MTS) were identified as urinary marker candidates through comprehensive analysis using liquid chromatography-mass spectrometry. The concentrations of these marker candidates and conventional markers were then compared among controls, patients, and numerous risk groups to develop a scoring system. Participants were classified into four groups: control, low risk, intermediate risk, and high risk, and the proportional odds model was fitted using the L2-penalized maximum likelihood method, incorporating age on a monthly scale for adjustment. This scoring model using the novel urine catecholamine metabolite combinations, VLA and MTS, had greater area under the curve values than the model using HVA and VMA for diagnosis (0.978 vs. 0.964), pretreatment risk assessment (low and intermediate risk vs. high risk: 0.866 vs. 0.724; low risk vs. intermediate and high risk: 0.871 vs. 0.680), and prognostic factors (MYCN status: 0.741 vs. 0.369, histology: 0.932 vs. 0.747). The new system also had greater accuracy in detecting missing high-risk neuroblastomas, and in predicting the pretreatment risk at the time of screening. The new scoring system employing VLA and MTS has the potential to replace the conventional adjunctive diagnostic method using HVA and VMA.

Urinary fatty acid biomarkers for prostate cancer detection.

The lack of accuracy in the current prostate specific antigen (PSA) test for prostate cancer (PCa) screening causes around 60-75% of unnecessary prostate biopsies. Therefore, alternative diagnostic methods that have better accuracy and can prevent over-diagnosis of PCa are needed. Researchers have examined various potential biomarkers for PCa, and of those fatty acids (FAs) markers have received special attention due to their role in cancer metabolomics. It has been noted that PCa metabolism prefers FAs over glucose substrates for continued rapid proliferation. Hence, we proposed using a urinary FAs based model as a non-invasive alternative for PCa detection. Urine samples collected from 334 biopsy-designated PCa positive and 232 biopsy-designated PCa negative subjects were analyzed for FAs and lipid related compounds by stir bar sorptive extraction coupled with gas chromatography/mass spectrometry (SBSE-GC/MS). The dataset was split into the training (70%) and testing (30%) sets to develop and validate logit models and repeated for 100 runs of random data partitioning. Over the 100 runs, we confirmed the stability of the models and obtained optimal tuning parameters for developing the final FA based model. A PSA model using the values of the patients' PSA test results was constructed with the same cohort for the purpose of comparing the performances of the FA model against PSA test. The FA final model selected 20 FAs and rendered an AUC of 0.71 (95% CI = 0.67-0.75, sensitivity = 0.48, and specificity = 0.83). In comparison, the PSA model performed with an AUC of 0.51 (95% CI = 0.46-0.66, sensitivity = 0.44, and specificity = 0.71). The study supports the potential use of urinary FAs as a stable and non-invasive alternative test for PCa diagnosis.

The Elusive Horizon: Biomarkers in Urothelial Carcinoma.

Urinary tests for circulating tumor DNA have potential for accurate discrimination of bladder cancer from other common inflammatory processes. Efforts are still needed to determine whether these tests can differentiate between cancer and field cancerization and to demonstrate clinical benefit in prospective trials.

Artificial intelligence assisted patient blood and urine droplet pattern analysis for non-invasive and accurate diagnosis of bladder cancer.

Bladder cancer is one of the most common cancer types in the urinary system. Yet, current bladder cancer diagnosis and follow-up techniques are time-consuming, expensive, and invasive. In the clinical practice, the gold standard for diagnosis remains invasive biopsy followed by histopathological analysis. In recent years, costly diagnostic tests involving the use of bladder cancer biomarkers have been developed, however these tests have high false-positive and false-negative rates limiting their reliability. Hence, there is an urgent need for the development of cost-effective, and non-invasive novel diagnosis methods. To address this gap, here we propose a quick, cheap, and reliable diagnostic method. Our approach relies on an artificial intelligence (AI) model to analyze droplet patterns of blood and urine samples obtained from patients and comparing them to cancer-free control subjects. The AI-assisted model in this study uses a deep neural network, a ResNet network, pre-trained on ImageNet datasets. Recognition and classification of complex patterns formed by dried urine or blood droplets under different conditions resulted in cancer diagnosis with a high specificity and sensitivity. Our approach can be systematically applied across droplets, enabling comparisons to reveal shared spatial behaviors and underlying morphological patterns. Our results support the fact that AI-based models have a great potential for non-invasive and accurate diagnosis of malignancies, including bladder cancer.

Early diagnosis and prognostic potential of RAC3 in bladder tumor.

BACKGROUND AND PURPOSE: Bladder tumors are among the most prevalent malignancies in the urinary system, and RAC3 has been linked to various types of cancer. This article seeks to explore the potential of RAC3 as both an early diagnostic marker for bladder tumors and a novel therapeutic target. METHODS/PATIENTS: The expression of RAC3 in bladder tissue was detected using immunohistochemical staining. Additionally, the protein expression of RAC3 was measured and quantified through enzyme-linked immunosorbent assay (ELISA). Subsequently, the correlation between the expression level of RAC3 and bladder tumors was investigated through multifactorial analysis and survival analysis. RESULTS: Our findings revealed that RAC3 expression was upregulated in bladder tumor tissues. Moreover, we observed higher levels of RAC3 expression in the serum and urine of patients with bladder tumors compared to those with non-bladder tumors. Additionally, we identified a significant positive correlation between RAC3 expression levels and the stage, degree of differentiation, and infiltration of bladder tumors. Importantly, high RAC3 expression emerged as an influential factor in the poor prognosis of bladder tumors, as patients with high RAC3 expression exhibited a lower overall survival rate than those with low RAC3 expression. CONCLUSION: Based on our results, RAC3 shows promise as both a marker for early diagnosis of bladder tumors and a potential therapeutic target.

Budgetary Impact of Including the Urinary Genomic Marker Cxbladder Detect in the Evaluation of Microhematuria Patients.

INTRODUCTION: Current AUA guidelines mandate a risk-stratified approach for the evaluation of microhematuria. Urine genomic tests with high negative predictive value could further reduce unnecessary diagnostic testing and morbidity, but the economic impact is unknown. This study modeled the financial impact of Cxbladder Detect on microhematuria evaluations. METHODS: A decision tree analysis was constructed by Coreva Scientific comparing 1-year costs of the standard microhematuria evaluation using the AUA guidelines vs an algorithm incorporating Cxbladder Detect. Cxbladder Detect-positive patients had cystoscopy and imaging, whereas patients with negative tests were reevaluated in 6 months. Patients with positive diagnostic testing underwent cystoscopy, and positive cystoscopies led to transurethral resection of bladder tumor. Test performance was based on published literature, and costs were based on Medicare allowable fees. RESULTS: Using the decision tree model, the average savings of using Cxbladder Detect was $559 compared with the standard of care, with an average reduction of 0.38 procedures per patient. Probabilistic analysis showed statistical significance with a median reduction in the total cost of $498 per patient (95% CrI [-1356, -2]) and a significant median reduction in diagnostic procedures per patient of 0.36 (95% CrI [-0.52, -0.16]) without impact on the number of cancers diagnosed. CONCLUSIONS: This model-based study demonstrates the potential economic value of using a Cxbladder-driven protocol for microhematuria evaluations.

New Molecular Markers for Prostate Cancer Diagnosis.

PURPOSE: Prostate cancer (PCa) is the second most commonly diagnosed cancer and the sixth leading cause of cancer death among men worldwide. Biomarkers are an important tool in the early detection of PCa. Prostate-specific antigen (PSA) is one of the oldest biomarkers for the early detection of PCa. Digital rectal exam (DRE) is another screening test for PCa detection, which is considered as an irritating experience for patients. Biopsy is still the most reliable method for PCa diagnosis; however, patients are prone to complications. Therefore, developing non-invasive and accurate methods for PCa screening seems urgent to avoid unnecessary biopsies. There has been remarkable development in PCa molecular biomarkers discovery, largely through progress in omics technologies. Due to the many benefits of liquid biopsies, a significant set of PCa diagnostic kits have been developed using urine samples. Despite the unique benefits of these kits, there are still many challenges to their widespread use in clinics. Here, we have reviewed the latest developments of PCa biomarkers in liquid biopsies. METHODS: Literature on biomarkers for diagnosis of PCa was reviewed during the past two decades. RESULTS: PSA, PHI, PCA3, and 4K score are among the commonly used markers for PCa diagnosis which have been used over a long-moderate length of time with multiple studies on their performance. We performed a review of their performance. Newer markers are among RNA and DNA markers. Multiple non-coding RNAs (mi-RNAs) were reviewed and their performance on Pca diagnosis was reviewed. Long noncoding RNAs (Lnc RNAs) including PlncRNA-1, HOTAIR, SchLAP-1, MALAT1, MEG3, and PRCAT17.3 were summarized. mRNA markers including TMPRSS2:ERG, and HOXC6 were presented. DNA-based markers including PTEN, HOXB13, and BRCA2 were reviewed. Finally, the use of CircRNAs was reviewed for PCa diagnosis. CONCLUSION: Many reviewed RNA-based biomarkers have promising results in the diagnosis of PCa.

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.

Proteomics Profiling of Bladder Cancer Tissues from Early to Advanced Stages Reveals NNMT and GALK1 as Biomarkers for Early Detection and Prognosis of BCa.

The high recurrence rate and invasive diagnostic and monitoring methods in bladder cancer (BCa) clinical management require the development of new non-invasive molecular tools for early detection, particularly for low-grade and low-stage BCa as well as for risk stratification. By using an in-solution digestion method and label-free data-independent LC-MS/MS coupled with ion mobility, we profiled the BCa tissues from initiation to advanced stages and confidently identified and quantified 1619 proteins (≥2 peptides). A statistically significant difference in abundance (Anova ≤ 0.05) showed 494 proteins. Significant correlation with stage with steady up or down with BCa stages showed 15 proteins. Testing of NNMT, GALK1, and HTRA1 in urine samples showed excellent diagnostic potential for NNMT and GALK1 with AUC of 1.000 (95% CI: 1.000-1.000; p < 0.0001) and 0.801 (95% CI: 0.655-0.947; p < 0.0001), respectively. NNMT and GALK1 also showed very good potential in discriminating non-invasive low-grade from invasive high-grade BCa with AUC of 0.763 (95% CI: 0.606-0.921; p = 0.001) and 0.801 (95% CI: 0.653-0.950; p < 0.0001), respectively. The combination of NNMT and GALK1 increased prognostic accuracy (AUC = 0.813). Our results broaden the range of potential novel candidates for non-invasive BCa diagnosis and prognosis.