Noninvasive Urine-Based Tests to Diagnose or Detect Recurrence of Bladder Cancer

Nanobody-based double antibody sandwich colloidal gold test strips for rapid detection of urinary biomarkers NMP22 and survivin in bladder cancer

Early non-invasive diagnosis of bladder cancer (BLCA) via urine biomarkers is a critical area in in vitro diagnostics. However, single-biomarker detection methods have limited specificity and sensitivity. To address this, we first developed two lateral-flow immunoassays utilizing nanobodies specific to two BLCA biomarkers, NMP22 (Nuclear Matrix Protein-22) and survivin. The assays employ a sandwich-format design in which nanobodies replace traditional antibodies, providing high stability and low background interference. Clinical evaluation demonstrated that NMP22 detection achieved 81.8 % sensitivity and 90.9 % specificity, while survivin detection yielded 72.7 % sensitivity and 100 % specificity. Both assays showed no significant differences compared to commercial ELISA kits (p > 0.05). Notably, multiplex detection using both strips achieved 100.0 % sensitivity and 90.9 % specificity, demonstrating superior performance compared to single-biomarker approaches. The strips are simple to prepare and cost-effective, offering a new method for the rapid screening and early diagnosis of BLCA.

Artificial intelligence algorithms enhance urine cytology reporting confidence in postoperative follow-up for upper urinary tract urothelial carcinoma

PURPOSE

In Taiwan, the incidence of urothelial carcinoma of the upper urinary tract (UTUC) is high and intravesical recurrence is approximately 22%-47%. Thus, postoperative cystoscopy and urine cytology follow-up, which require experienced cytologists, are needed. The AIxURO system aligns with The Paris System (TPS) guidelines for reporting urinary cytology. This study assessed the benefit of early detection of intravesical recurrence after nephroureterectomy using the AIxURO system.

METHODS

Urine cytology slides (n = 296) from 113 patients with UTUC were retrieved and patient data, pre-operative and postoperative cytology, pathology, and follow-up series for the intravesical recurrence group were collected. Cytology slides were digitized and independently assessed by the AIxURO system, and the results were compared to cytology reports.

RESULTS

From January 2022 to August 2023, 113 patients with UTUC underwent nephroureterectomy. Eighty-eight patients (88/113, 77.8%) received 1 or 2 cytology examinations pre-operatively, 44 slides (44/204, 21.5%) were positive and 34 patients (34/113, 30.1%) were diagnosed with UTUC. Postoperative intravesical recurrence was detected in 27 patients (27/113, 23%) at an average of 190.62 days. Thirty-four slides (34/56, 60.7%) were negative for UTUC. Eight patients (8/27, 29.6%) met the criteria for early diagnosis of intravesical recurrence. The AIxURO system identified two more patients (10/27, 37.0%) with early intravesical recurrence.

CONCLUSION

The AIxURO system enhanced postoperative urine cytology reporting confidence and could identify some underdiagnosed slides to enhance the early detection of UTUC with intravesical recurrence. AIxURO may be used for post-nephroureterectomy follow-up and reduce the necessity for cystoscopy.

Non-invasive early-stage cancer detection: current methods and future perspectives

This review paper explores the realm of non-invasive methods for early cancer detection. Early identification is crucial for effective therapeutic intervention, and non-invasive techniques have emerged as promising tools to enhance diagnostic accuracy and improve patient outcomes. The paper thoroughly examines the advantages, limitations, and prospects of various non-invasive approaches, including blood tests, non-blood-based tests, and diverse imaging modalities. It discusses the biomarkers found in blood for early-stage cancer detection, specifying the types of cancer associated with each biomarker. The non-blood-based tests focus on components in saliva, urine, and breath for cancer detection, alongside current studies and future perspectives on various cancers. Optical imaging methods covered in this review include fluorescence imaging in the near-infrared (NIR) region, bioluminescence imaging, and Raman spectroscopy for early-stage cancer detection. The review also highlights the pros and cons of ultrasound imaging in early-stage cancer detection. Additionally, the clinical implications of using AI for cancer detection, both present and future, are explored. This paper provides valuable insights for researchers and clinicians working in the field of non-invasive early-stage cancer detection.

Predicting distant metastasis of bladder cancer using multiple machine learning models: a study based on the SEER database with external validation

Background and purpose

Distant metastasis in bladder cancer is linked to poor prognosis and significant mortality. Machine learning (ML), a key area of artificial intelligence, has shown promise in the diagnosis, staging, and treatment of bladder cancer. This study aimed to employ various ML techniques to predict distant metastasis in patients with bladder cancer.

Patients and methods

Patients diagnosed with bladder cancer in the Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2021 were included in this study. After a rigorous screening process, a total of 4,108 patients were selected for further analysis, divided in a 7:3 ratio into a training cohort and an internal validation cohort. In addition, 118 patients treated at the Second Affiliated Hospital of Nanchang University were included as an external validation cohort. Features were filtered using the least absolute shrinkage and selection operator (LASSO) regression algorithm. Based on the significant features identified, three ML algorithms were utilized to develop prediction models: logistic regression, support vector machine (SVM), and linear discriminant analysis (LDA). The predictive performance of the three models was evaluated by obtaining the area under the receiver operating characteristic (ROC) curve (AUC), the precision, the accuracy, and the F1 score.

Results

According to the statistical results, the final probability of distant metastasis in the population was 12.0% (n = 495). LASSO regression analysis revealed that age, chemotherapy, tumor size, the examination of non-regional lymph nodes, and regional lymph node evaluation were significantly associated with distant metastasis of bladder cancer. In the internal validation cohort, the prediction accuracy rates for logistic regression, SVM, and LDA were 0.874, 0.877, and 0.845, respectively. The precision rates were 0.805, 0.769, and 0.827, respectively, and the F1 scores were 0.821, 0.819, and 0.835, respectively. The ROC curve demonstrated that the AUC for all models was greater than 0.7. In the external validation cohort, the prediction accuracy rates for logistic regression, SVM, and LDA were 0.856, 0.848, and 0.797, respectively, with the ROC curve indicating that the AUC also exceeded 0.7. The precision rates were 0.877, 0.718, and 0.736, respectively, and the F1 scores were 0.797, 0.778, and 0.762, respectively. Among the algorithms used, logistic regression demonstrated better predictive efficiency than the other two methods. The top three variables with the highest importance scores in the logistic regression were non-regional lymph nodes, age, and chemotherapy.

Conclusion

The prediction model developed using three ML algorithms demonstrated strong accuracy and discriminative capability in predicting distant metastasis in patients with bladder cancer. This might help clinicians in understanding patient prognosis and in formulating personalized treatment strategies, ultimately improving the overall prognosis of patients with bladder cancer.

SynNotch-Programmed Macrophages for Cancerous Cell Detection and Sensing

Synthetic Notch (synNotch) receptors have enabled mammalian cells to sense extracellular ligands and respond by activating user-prescribed transcriptional programs. Based on the synNotch system, we describe a cell-based in vivo sensor for cancerous cell detection. We attempted to engineer synNotch-programmed macrophages to sense cancer cells via urinary analysis of human chorionic gonadotropin (HCGB5). Principally, when the synNotch receptors of macrophages bind to the ligands of cancer cells, Notch is activated and undergoes intramembrane proteolysis to release the transcriptional activator into the nucleus. The transcriptional activator targets and activates downstream gene expression, such as human chorionic gonadotropin (HCGB5) in macrophages. When HCGB5 is secreted extracellularly into urine, it can be detected with commercial HCGB5 colloidal gold test strips. As a proof of principle, we demonstrated the feasibility of synNotch-programmed macrophages in detecting breast cancer cells engineered with artificial EGFP ligands. We demonstrated that HCGB5 expression was only induced when the cancer cell expressing EGFP ligands is present; thereby, extracellular HCGB5 expression is directly proportional to the number of cancer cells. Further optimizations of the synNotch system can realize the ultimate goal of establishing cell-based in vivo sensors as the paragon of cancer diagnostics for point-of-care testing and home self-test.

Self-Sustaining Triboelectric Nanosensors for Real-Time Urine Analysis in Smart Toilets

Healthcare has undergone a revolutionary shift with the advent of smart technologies, and smart toilets (STs) are among the innovative inventions offering non-invasive continuous health monitoring. The present technical challenges toward this development include limited sensitivity of integrated sensors, poor stability, slow response and the requirement external energy supply alongside manual sample collection. In this article, triboelectric nanosensor array (TENSA) is introduced featuring electrodes crafted from laser-induced 3D graphene with functional polymers like polystyrene, polyimide, and polycaprolactone for real-time urine analysis while generating 50 volts output via urine droplet-based triboelectrification. Though modulating interfacial double-layer capacitance, these sensors exhibit exceptional sensitivity and selectivity in detecting a broad spectrum of urinary biomarkers, including ions, glucose, and urea with a classification precision of 95% and concentration identification accuracy of up to 0.97 (R2), supported by artificial neural networks. Upon exposure to urine samples containing elevated levels of Na+, K+, and NH4 +, a notable decrease (ranging from 32% to 68%) is observed in output voltages. Conversely, urea induces an increase up to 13%. Experimental validation confirms the stability, robustness, reliability, and reproducibility of TENSA, representing a significant advancement in healthcare technology, offering the potential for improved disease management and prevention strategies.

Renal Cell Carcinoma Discrimination through Attenuated Total Reflection Fourier Transform Infrared Spectroscopy of Dried Human Urine and Machine Learning Techniques

Renal cell carcinoma (RCC) is the sixth most common cancer in men and is often asymptomatic, leading to incidental detection in advanced disease stages that are associated with aggressive histology and poorer outcomes. Various cancer biomarkers are found in urine samples from patients with RCC. In this study, we propose to investigate the use of Attenuated Total Reflection-Fourier Transform Infrared Spectroscopy (ATR-FTIR) on dried urine samples for distinguishing RCC. We analyzed dried urine samples from 49 patients with RCC, confirmed by histopathology, and 39 healthy donors using ATR-FTIR spectroscopy. The vibrational bands of the dried urine were identified by comparing them with spectra from dried artificial urine, individual urine components, and dried artificial urine spiked with urine components. Urea dominated all spectra, but smaller intensity peaks, corresponding to creatinine, phosphate, and uric acid, were also identified. Statistically significant differences between the FTIR spectra of the two groups were obtained only for creatinine, with lower intensities for RCC cases. The discrimination of RCC was performed through Principal Component Analysis combined with Linear Discriminant Analysis (PCA-LDA) and Support Vector Machine (SVM). Using PCA-LDA, we achieved a higher discrimination accuracy (82%) (using only six Principal Components to avoid overfitting), as compared to SVM (76%). Our results demonstrate the potential of urine ATR-FTIR combined with machine learning techniques for RCC discrimination. However, further studies, especially of other urological diseases, must validate this approach.

Rapid Diagnosis of Urinary Tract Cancers on a LEGO-Inspired Detection Platform via Chemiresistive Profiling of Volatile Metabolites

Rapid and in situ profiling of volatile metabolites from body fluids represents a new trend in cancer diagnosis and classification in the early stages. We report herein an on-chip strategy that combines an array of conductive nanosensors with a chaotic gas micromixer for real-time monitoring of volatiles from urine and for accurate diagnosis and classification of urinary tract cancers. By integrating a class of LEGO-inspired microchambers immobilized with MXene-based sensing nanofilms and zigzag microfluidic gas channels, it enables the intensive intermingling of volatile organic chemicals with sensor elements that tremendously facilitate their ion-dipole interactions for molecular recognition. Aided with an all-in-one, point-of-care platform and an effective machine-learning algorithm, healthy or diseased samples from subpopulations (i.e., tumor subtypes, staging, lymph node metastasis, and distant metastasis) of urinary tract cancers can be reliably fingerprinted in a few minutes with high sensitivity and specificity. The developed detection platform has proven to be a noninvasive supplement to the liquid biopsies available for facile screening of urinary tract cancers, which holds great potential for large-scale personalized healthcare in low-resource areas.

Integrated multiomics analysis of chromosome 19 miRNA cluster in bladder cancer

With 46 microRNAs (miRNAs) embedded tandemly over a distance of ~100 kb, chromosome 19 microRNA cluster (C19MC) is the largest miRNA cluster in the human genome. The C19MC is transcribed from a long noncoding genomic region and is usually expressed simultaneously at a higher level. Hence, we performed an integrative multiomics data analysis to examine C19MC regulation, expression patterns, and their impact on bladder cancer (BCa). We found that 43 members of C19MC were highly expressed in BCa. However, its co-localization with recurrent copy number variation (CNV) gain was not statistically significant to implicate its upregulation. It has been reported that C19MC expression is regulated by a well-established CpG island situated 17.6 kb upstream of the transcription start site, but we found that CpG probes at this island were hypomethylated, which was not statistically significant in the BCa cohort. In addition, the promoter region of C19MC is strongly regulated by a group of seven transcription factors (NR2F6, SREBF1, TBP, GATA3, GABPB1, ETV4, and ZNF444) and five chromatin modifiers (SMC3, KDMA1, EZH2, RAD21, and CHD7). Interestingly, these 12 genes were found to be overexpressed in BCa patients. Further, C19MC targeted 42 tumor suppressor (TS) genes that were downregulated, of which 15 were significantly correlated with patient survival. Our findings suggest that transcription factors and chromatin modifiers at the promoter region may regulate C19MC overexpression. The upregulated C19MC members, transcription regulators, and TS genes can be further exploited as potential diagnostic and prognostic indicators as well as for therapeutic management of BCa.

Nanotechnology Involved in Treating Urinary Tract Infections: An Overview

Considered as the most frequent contaminations that do not require hospitalization, urinary tract infections (UTIs) are largely known to cause significant personal burdens on patients. Although UTIs overall are highly preventable health issues, the recourse to antibiotics as drug treatments for these infections is a worryingly spread approach that should be addressed and gradually overcome in a contemporary, modernized healthcare system. With a virtually alarming global rise of antibiotic resistance overall, nanotechnologies may prove to be the much-needed 'lifebuoy' that will eventually suppress this prejudicial phenomenon. This review aims to present the most promising, currently known nano-solutions, with glimpses on clinical and epidemiological aspects of the UTIs, prospective diagnostic instruments, and non-antibiotic treatments, all of these engulfed in a comprehensive overview.

The Roles of miRNAs in Predicting Bladder Cancer Recurrence and Resistance to Treatment

Bladder cancer (BCa) is associated with significant morbidity, with development linked to environmental, lifestyle, and genetic causes. Recurrence presents a significant issue and is managed in the clinical setting with intravesical chemotherapy or immunotherapy. In order to address challenges such as a limited supply of BCG and identifying cases likely to recur, it would be advantageous to use molecular biomarkers to determine likelihood of recurrence and treatment response. Here, we review microRNAs (miRNAs) that have shown promise as predictors of BCa recurrence. MiRNAs are also discussed in the context of predicting resistance or susceptibility to BCa treatment.

Identification and validation of methylated PENK gene for early detection of bladder cancer using urine DNA

Background

Early detection of bladder cancer (BCa) offers patients a favorable outcome and avoids the need for cystectomy. Development of an accurate and sensitive noninvasive BCa diagnostic test is imperative. DNA methylation is an early epigenetic event in the development of BCa. Certain specific aberrant methylations could serve as useful biomarkers. The aim of this study was to identify methylation biomarkers for early detection of BCa.

Methods

CpG methylation microarray analysis was conducted on primary tumors with varying stages (T1—T4) and paired nontumor tissues from nine BCa patients. Bisulfite-pyrosequencing was performed to confirm the methylation status of candidate genes in tissues and urine sediments (n = 51). Among them, PENK was selected as a potential candidate and validated using an independent set of 169 urine sediments (55 BCa, 25 benign urologic diseases, 8 other urologic cancers, and 81 healthy controls) with a quantitative methylation-specific real time PCR (mePENK-qMSP). All statistical analyses were performed using MedCalc software version 9.3.2.0.

Results

CpG methylation microarray analysis and stepwise validation by bisulfite-pyrosequencing for tissues and urine sediments supported aberrant methylation sites of the PENK gene as potential biomarkers for early detection of BCa. Clinical validation of the mePENK-qMSP test using urine sediment-DNA showed a sensitivity of 86.5% (95% CI: 71.2 – 95.5%), a specificity of 92.5% (95% CI: 85.7 – 96.7%), and an area under ROC of 0.920 (95% CI: 0.863 – 0.959) in detecting Ta high-grade and advanced tumor stages (T1-T4) of BCa patients. Sensitivities for Ta low-grade, Ta high-grade, T1 and T2-T4 were 55.6, 83.3, 88.5, and 100%, respectively. Methylation status of PENK was not correlated with sex, age or stage, while it was associated with the tumor grade of BCa.

Conclusions

In this study, we analyzed the comprehensive patterns of DNA methylation identified that PENK methylation possesses a high potential as a biomarker for urine-based early detection of BCa. Validation of PENK methylation confirms that it could significantly improve the noninvasive detection of BCa.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-10275-2.

AHNAK2 Urinary Protein Expression as Potential Biomarker for Bladder Cancer Detection: A Pilot Study

OBJECTIVE

This study aimed to measure the AHNAK2 urinary levels in bladder cancer patients.

MATERIAL AND METHODS

This prospective case-control study enrolled 67 participants between January and March 2019 and were categorized into bladder cancer group (n=37), with histologically proven bladder can cer, and control group (n=30), with histologically verified benign lesions or with no bladder cancer indica tion during follow-up. Urine samples of 15 mL were collected in the mid-morning before cystoscopy/surger y and an enzyme-linked immunosorbent assay was performed as per the manufacturer's protocol. Bladder malignancies were classified according to the World Health Organization Tumor Classification. Group's associations were evaluated with the Student t-test, Spearman's rank correlation, and Mann-Whitney U test, while receiver operating curve was plotted for assessing the test's performance.

RESULTS

Mean age of the bladder cancer group was 66.41 years (standard deviation=10.04, range=43-82 years) and the control group was 59.67 years (standard deviation=10.44, range=38-77 years). All bladder cancers were of the urothelial histotype, with the following pT distribution: pTa/papillary urothelial neoplasm of low malignant potential (n=19; 28.4%), Primary tumor (pT) in situ (n=4; 6%), pT1 (n=7; 10.4%), and pT≥2 (n=7; 10.48%). Mean AHNAK2 levels were higher in bladder cancer patients 49.08 pg/mL (standard deviation=114.91) compared to controls 5.28 pg/mL (standard devia tion=6.65), P < .05). Significant differences were noted between non-invasive bladder cancer (n=23; mean=7.14 pg/mL; standard deviation=7.26) and invasive bladder cancer (n=14; mean=117.99 pg/mL; standard deviation=168.08) and between non-muscle invasive bladder cancer (mean=23.19 pg/mL; standard deviation=66.93) and muscle-invasive bladder cancer (mean=160.05 pg/mL; standard devia tion=199.65) (P < .001). The result of the assays was given as follows: sensitivity: 64.19%, specificity: 66.67%, positive predictive value: 22.07%, negative predictive value: 92.37%, area under curve: 0.695, and 95% CI: 0.57-0.82.

CONCLUSION

AHNAK2 protein could be used as bladder cancer surveillance biomarker. The inclusion of AHNAK2 levels in stratification nomograms might reduce the number of unnecessary cystoscopies.

Integrins are enriched on aberrantly fucosylated tumour-derived urinary extracellular vesicles

Urinary extracellular vesicles (uEVs) are enriched with glycosylated proteins which have been extensively studied as putative biomarkers of urological cancers. Here, we characterized the glycosylation and integrin profile of EVs derived from urological cancer cell lines. We used fluorescent europium-doped nanoparticles coated with lectins and antibodies to identify a biomarker combination consisting of integrin subunit alpha 3 (ITGA3) and fucose. In addition, we used the same cancer cell line-derived EVs as analytical standards to assess the sensitivity of the ITGA3-UEA assay. The clinical performance of the ITGA3-UEA assay was analysed using urine samples of various urological pathologies including diagnostically challenging benign prostatic hyperplasia (BPH), prostate cancer (PCa) and bladder cancer (BlCa). The assay can significantly discriminate BlCa from all other patient groups: PCa (9.2-fold; p = 0.00038), BPH (5.5-fold; p = 0.004) and healthy individuals (and 23-fold; p = 0.0001). Our results demonstrate that aberrantly fucosylated uEVs and integrin ITGA3 can be detected with fucose-specific lectin UEA in a simple bioaffinity assay for the detection of BlCa directly from unprocessed urine.

On an Affordable Approach towards the Diagnosis and Care for Prostate Cancer Patients Using Urine, FTIR and Prediction Machines

Prostate cancer is a widespread form of cancer that affects patients globally and is challenging to diagnose, especially in its early stages. The common means of diagnosing cancer involve mostly invasive methods, such as the use of patient’s blood as well as digital biopsies, which are relatively expensive and require a considerable amount of expertise. Studies have shown that various cancer biomarkers can be present in urine samples from patients who have prostate cancers; this paper aimed to leverage this information and investigate this further by using urine samples from a group of patients alongside FTIR analysis for the prediction of prostate cancer. This investigation was carried out using three sets of data where all spectra were preprocessed with the linear series decomposition learner (LSDL) and post-processed using signal processing methods alongside a contrast across nine machine-learning models, the results of which showcased that the proposed modeling approach carries potential to be used for clinical prediction of prostate cancer. This would allow for a much more affordable and high-throughput means for active prediction and associated care for patients with prostate cancer. Further investigations on the prediction of cancer stage (i.e., early or late stage) were carried out, where high prediction accuracy was obtained across the various metrics that were investigated, further showing the promise and capability of urine sample analysis alongside the proposed and presented modeling approaches.

Diagnostic and prognostic potential clustered miRNAs in bladder cancer

At specific genomic loci, miRNAs are in clusters and their association with copy number variations (CNVs) may exhibit abnormal expression in several cancers. Hence, the current study aims to understand the expression of miRNA clusters residing within CNVs and the regulation of their target genes in bladder cancer. To achieve this, we used extensive bioinformatics resources and performed an integrated analysis of recurrent CNVs, clustered miRNA expression, gene expression, and drug-gene interaction datasets. The study identified nine upregulated miRNA clusters that are residing on CNV gain regions and three miRNA clusters (hsa-mir-200c/mir-141, hsa-mir-216a/mir-217, and hsa-mir-15b/mir-16-2) are correlated with patient survival. These clustered miRNAs targeted 89 genes that were downregulated in bladder cancer. Moreover, network and gene enrichment analysis displayed 10 hub genes (CCND2, ETS1, FGF2, FN1, JAK2, JUN, KDR, NOTCH1, PTEN, and ZEB1) which have significant potential for diagnosis and prognosis of bladder cancer patients. Interestingly, hsa-mir-200c/mir-141 and hsa-mir-15b/mir-16-2 cluster candidates showed significant differences in their expression in stage-specific manner during cancer progression. Downregulation of NOTCH1 by hsa-mir-200c/mir-141 may also sensitize tumors to methotrexate thus suggesting potential chemotherapeutic options for bladder cancer subjects. To overcome some computational challenges and reduce the complexity in multistep big data analysis, we developed an automated pipeline called CmiRClustFinder v1.0 (https://github.com/msls-bioinfo/CmiRClustFinder_v1.0), which can perform integrated data analysis of 35 TCGA cancer types.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s13205-022-03225-z.

Non-invasive diagnosis and surveillance of bladder cancer with driver and passenger DNA methylation in a prospective cohort study

BACKGROUND

State-of-art non-invasive diagnosis processes for bladder cancer (BLCA) harbour shortcomings such as low sensitivity and specificity, unable to distinguish between high- (HG) and low-grade (LG) tumours, as well as inability to differentiate muscle-invasive bladder cancer (MIBC) and non-muscle-invasive bladder cancer (NMIBC). This study investigates a comprehensive characterization of the entire DNA methylation (DNAm) landscape of BLCA to determine the relevant biomarkers for the non-invasive diagnosis of BLCA.

METHODS

A total of 304 samples from 224 donors were enrolled in this multi-centre, prospective cohort study. BLCA-specific DNAm signature discovery was carried out with genome-wide bisulfite sequencing in 32 tumour tissues and 12 normal urine samples. A targeted sequencing assay for BLCA-specific DNAm signatures was developed to categorize tumour tissue against normal urine, or MIBC against NMIBC. Independent validation was performed with targeted sequencing of 259 urine samples in a double-blinded manner to determine the clinical diagnosis and prognosis value of DNAm-based classification models. Functions of genomic region harbouring BLCA-specific DNAm signature were validated with biological assays. Concordances of pathology to urine tumour DNA (circulating tumour DNA [ctDNA]) methylation, genomic mutations or other state-of-the-art diagnosis methods were measured.

RESULTS

Genome-wide DNAm profile could accurately classify LG tumour from HG tumour (LG NMIBC vs. HG NMIBC: p = .038; LG NMIBC vs. HG MIBC, p = .00032; HG NMIBC vs. HG MIBC: p = .82; Student's t-test). Overall, the DNAm profile distinguishes MIBC from NMIBC and normal urine. Targeted-sequencing-based DNAm signature classifiers accurately classify LG NMIBC tissues from HG MIBC and could detect tumours in urine at a limit of detection of less than .5%. In tumour tissues, DNAm accurately classifies pathology, thus outperforming genomic mutation or RNA expression profiles. In the independent validation cohort, pre-surgery urine ctDNA methylation outperforms fluorescence in situ hybridization (FISH) assay to detect HG BLCA (n = 54) with 100% sensitivity (95% CI: 82.5%-100%) and LG BLCA (n = 26) with 62% sensitivity (95% CI: 51.3%-72.7%), both at 100% specificity (non-BLCA: n = 72; 95% CI: 84.1%-100%). Pre-surgery urine ctDNA methylation signature correlates with pathology and predicts recurrence and metastasis. Post-surgery urine ctDNA methylation (n = 61) accurately predicts recurrence-free survival within 180 days, with 100% accuracy.

CONCLUSION

With the discovery of BLCA-specific DNAm signatures, targeted sequencing of ctDNA methylation outperforms FISH and DNA mutation to detect tumours, predict recurrence and make prognoses.

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer

Simple Summary

Bladder cancer is evidently a challenge as far as its prognosis and treatment are concerned. The investigation of potential biomarkers and therapeutic targets is indispensable and still in progress. Most studies attempt to identify differential signatures between distinct molecular tumor subtypes. Therefore, keeping in mind the heterogeneity of urinary bladder tumors, we attempted to identify a consensus gene-related signature between the common expression profile of bladder cancer and control samples. In the quest for substantive features, we were able to identify key hub genes, whose signatures could hold diagnostic, prognostic, or therapeutic significance, but, primarily, could contribute to a better understanding of urinary bladder cancer biology.

Abstract

Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high morbidity and mortality. This study intended to elucidate potential key biomarkers related to the occurrence, development, and prognosis of BCa through an integrated bioinformatics analysis. In this context, a systematic meta-analysis, integrating 18 microarray gene expression datasets from the GEO repository into a merged meta-dataset, identified 815 robust differentially expressed genes (DEGs). The key hub genes resulted from DEG-based protein–protein interaction and weighted gene co-expression network analyses were screened for their differential expression in urine and blood plasma samples of BCa patients. Subsequently, they were tested for their prognostic value, and a three-gene signature model, including COL3A1, FOXM1, and PLK4, was built. In addition, they were tested for their predictive value regarding muscle-invasive BCa patients’ response to neoadjuvant chemotherapy. A six-gene signature model, including ANXA5, CD44, NCAM1, SPP1, CDCA8, and KIF14, was developed. In conclusion, this study identified nine key biomarker genes, namely ANXA5, CDT1, COL3A1, SPP1, VEGFA, CDCA8, HJURP, TOP2A, and COL6A1, which were differentially expressed in urine or blood of BCa patients, held a prognostic or predictive value, and were immunohistochemically validated. These biomarkers may be of significance as prognostic and therapeutic targets for BCa.

Detection of ADAM15 in urine from patients with bladder cancer

Cancer is one of the leading causes of death in the United States and Europe. Of the cancers, bladder cancer is the 10th most frequently diagnosed cancer and the 13th most frequently diagnosed cancer in men. There are many studies showing that proteolytic enzymes, e.g. A Disintegrin and Metalloproteinases (ADAMs), play a key role in the development and progression of neoplasms. In this paper, we present the use of chromogenic substrate of ADAM15 for the qualitative determination of specific activity of enzyme in urine of patients with confirmed bladder cancer. In the first step, we optimized the substrate molecule in non-primed positions using combinatorial chemistry. By means of the obtained ABZ-His-Ala-Arg-Gly-ANB-NH₂ peptide, we detected ADAM15 activity in urine samples collected from patients diagnosed with bladder cancer. In contrast, we did not observe such activity in urine obtained from healthy volunteers.

Characterization of the Peri-Membrane Fluorescence Phenomenon Allowing the Detection of Urothelial Tumor Cells in Urine

Simple Summary

To detect bladder cancer (BC), urinary cytology and cystoscopy are the primary diagnostic tests used. Urine cytology is non-invasive, easy to collect, with medium sensitivity and high specificity. It is an effective way to detect high-grade BC, but it is less effective on low-grade BC because the rate of equivocal results is much higher, making them difficult to detect. Despite the implementation of new diagnostics, urinary cytology and cystoscopy remain the gold standard. Instead of looking for new diagnostics, one of the new research areas is the improvement of urinary cytology. Recently, the fluorescent properties of plasma membranes of urothelial tumor cells, called peri-membrane fluorescence, found in urinary cytology have been shown to be useful in improving the early detection of BC. The main objective of this study was to characterize the peri-membrane fluorescence allowing the detection of urothelial tumor cells in urine.

Abstract

Urine cytology is non-invasive, easy to collect, with medium sensitivity and a high specificity. It is an effective way to detect high-grade bladder cancer (BC), but it is less effective on low-grade BC because the rate of equivocal results is much higher. Recently, the fluorescent properties of plasma membranes of urothelial tumor cells (UTC) found in urine cytology have been shown to be useful in improving the early detection of BC. This phenomenon is called peri-membrane fluorescence (PMF). Based on previous studies that have identified the PMF on UTCs, the main objective was to characterize this phenomenon. For this study, a software was specially created to quantify the PMF of all tested cells and different treatments performed. PMF was not found to be a morphological and discriminating feature of UTCs, all cells in shape and not from urine show PMF. We were able to highlight the crucial role of plasma membrane integrity in the maintenance of PMF. Finally, it was found that the induction of a strong cellular stress induced a decrease in PMF, mimicking what was observed in non-tumor cells collected from urine. These results suggest that PMF is found in cells able to resist this stress, such as tumor cells.

Current Advances in N6-Methyladenosine Methylation Modification During Bladder Cancer

N6-methyladenosine (m6A) is a dynamic, reversible post-transcriptional modification, and the most common internal modification of eukaryotic messenger RNA (mRNA). Considerable evidence now shows that m6A alters gene expression, thereby regulating cell self-renewal, differentiation, invasion, and apoptotic processes. M6A methylation disorders are directly related to abnormal RNA metabolism, which may lead to tumor formation. M6A methyltransferase is the dominant catalyst during m6A modification; it removes m6A demethylase, promotes recognition by m6A binding proteins, and regulates mRNA metabolic processes. Bladder cancer (BC) is a urinary system malignant tumor, with complex etiology and high incidence rates. A well-differentiated or moderately differentiated pathological type at initial diagnosis accounts for most patients with BC. For differentiated superficial bladder urothelial carcinoma, the prognosis is normally good after surgery. However, due to poor epithelial cell differentiation, BC urothelial cell proliferation and infiltration may lead to invasive or metastatic BC, which lowers the 5-years survival rate and significantly affects clinical treatments in elderly patients. Here, we review the latest progress in m6A RNA methylation research and investigate its regulation on BC occurrence and development.

Application of SPRi Biosensors for Determination of 20S Proteasome and UCH-L1 Levels in the Serum and Urine of Transitional Bladder Cancer Patients

The ubiquitin–proteasome system (UPS) participates in the degradation of proteins which play an important role in regulating the cell cycle, apoptosis, and angiogenesis, as well as in the immune system. These processes are important in carcinogenesis. Transitional cell carcinoma (TCC) is one of the predominant types of bladder cancer. The relationship between the ubiquitin–proteasome system and cancer progression has become a topic of increasing interest among researchers. In this work, we propose an application of surface plasmon resonance imaging (SPRi)-based biosensors for the detection of 20S proteasome and ubiquitin carboxy-terminal hydrolase L1 (UCH-L1) in the blood serum and urine of patients with TCC. The aim of the study was to determine 20S proteasome and UCH-L1 concentrations and to correlate the results with clinicopathological parameters. The group of subjects consisted of 82 patients with confirmed TCC, in addition to a control group of 27 healthy volunteers. It was found that 20S proteasome and UCH-L1 concentrations were significantly elevated in both the serum and urine of TCC patients, compared with the healthy subjects. There was a correlation between 20S proteasome concentrations in serum and urine, as well as between serum proteasome and UCH-L1 concentration. The SPRi biosensor sensitive to 20S proteasome using PSI inhibitor as the receptor, and the SPRi biosensor sensitive to the UCH-L1 protein using the protein-specific antibody as the receptor is suitable for the determination of 20S proteasome and UCH-L1 in body fluids and can serve as useful tools in the investigation of cancer biomarkers.