Highly sensitive and specific novel biomarkers for the diagnosis of transitional bladder carcinoma.
ABSTRACT: Transitional bladder carcinoma (BCa) is prevalent in developed countries, particularly among men. Given that these tumors frequently recur or progress, the early detection and subsequent monitoring of BCa at different stages is critical. Current BCa diagnostic biomarkers are not sufficiently sensitive for substituting or complementing invasive cystoscopy. Here, we sought to identify a robust set of urine biomarkers for BCa detection. Using a high-resolution, mass spectrometry-based, quantitative proteomics approach, we measured, compared and validated protein variations in 451 voided urine samples from healthy subjects, non-bladder cancer patients and patients with non-invasive and invasive BCa. We identified five robust biomarkers: Coronin-1A, Apolipoprotein A4, Semenogelin-2, Gamma synuclein and DJ-1/PARK7. In diagnosing Ta/T1 BCa, these biomarkers achieved an AUC of 0.92 and 0.98, respectively, using ELISA and western blot data (sensitivity, 79.2% and 93.9%; specificity, 100% and 96.7%, respectively). In diagnosing T2/T3 BCa, an AUC of 0.94 and 1.0 was attained (sensitivity, 86.4% and 100%; specificity, 100%) using the same methods. Thus, our multiplex biomarker panel offers unprecedented accuracy for the diagnosis of BCa patients and provides the prospect for a non-invasive way to detect bladder cancer.
Project description:Previous researches pointed out that the measurement of urine fibronectin (Fn) could be a potential diagnostic test for bladder cancer (BCa). We conducted this meta-analysis to fully assess the diagnostic value of urine Fn for BCa detection.A systematic literature search in PubMed, ISI Web of Science, EMBASE, Cochrane library, and CBM was carried out to identify eligible studies evaluating the urine Fn in diagnosing BCa. Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) with their 95% confidence intervals (CIs) were calculated, and summary receiver operating characteristic (SROC) curves were established. We applied the STATA 13.0, Meta-Disc 1.4, and RevMan 5.3 software to the meta-analysis.Eight separate studies with 744 bladder cancer patients were enrolled in this meta-analysis. The pooled sensitivity, specificity, and DOR were 0.80 (95%CI = 0.77-0.83), 0.79 (95%CI = 0.73-0.84), and 15.18 (95%CI = 10.07-22.87), respectively, and the area under the curve (AUC) of SROC was 0.83 (95%CI = 0.79-0.86). The diagnostic power of a combined method (urine Fn combined with urine cytology) was also evaluated, and its sensitivity and AUC were significantly higher (0.86 (95%CI = 0.82-0.90) and 0.89 (95%CI = 0.86-0.92), respectively). Meta-regression along with subgroup analysis based on various covariates revealed the potential sources of the heterogeneity and the detailed diagnostic value of each subgroup. Sensitivity analysis supported that the result was robust. No threshold effect and publication bias were found in this meta-analysis.Urine Fn may become a promising non-invasive biomarker for bladder cancer with a relatively satisfactory diagnostic power. And the combination of urine Fn with cytology could be an alternative option for detecting BCa in clinical practice. The potential value of urine Fn still needs to be validated in large, multi-center, and prospective studies.
Project description:The development of accurate, non-invasive urinary assays for bladder cancer would greatly facilitate the detection and management of a disease that has a high rate of recurrence and progression. In this study, we employed a discovery and validation strategy to identify microRNA signatures that can perform as a non-invasive bladder cancer diagnostic assay. Expression profiling of 754 human microRNAs (TaqMan low density arrays) was performed on naturally voided urine samples from a cohort of 85 subjects of known bladder disease status (27 with active BCa). A panel of 46 microRNAs significantly associated with bladder cancer were subsequently monitored in an independent cohort of 121 subjects (61 with active BCa) using quantitative real-time PCR (RT-PCR). Multivariable modeling identified a 25-target diagnostic signature that predicted the presence of BCa with an estimated sensitivity of 87% at a specificity of 100% (AUC 0.982). With additional validation, the monitoring of a urinary microRNA biomarker panel could facilitate the non-invasive evaluation of patients under investigation for BCa.
Project description:The early detection of bladder cancer is important as the disease has a high rate of recurrence and progression. The development of accurate, non-invasive urinary assays would greatly facilitate detection. In previous studies, we have reported the discovery and initial validation of mRNA biomarkers that may be applicable in this context. In this study, we evaluated the diagnostic performance of proposed molecular signatures in an independent cohort.Forty-four mRNA transcripts were monitored blindly in urine samples obtained from a cohort of 196 subjects with known bladder disease status (89 with active BCa) using quantitative real-time PCR (RT-PCR). Statistical analyses defined associations of individual biomarkers with clinical data and the performance of predictive multivariate models was assessed using ROC curves. The majority of the candidate mRNA targets were confirmed as being associated with the presence of BCa over other clinical variables. Multivariate models identified an optimal 18-gene diagnostic signature that predicted the presence of BCa with a sensitivity of 85% and a specificity of 88% (AUC 0.935). Analysis of mRNA signatures in naturally micturated urine samples can provide valuable information for the evaluation of patients under investigation for BCa. Additional refinement and validation of promising multi-target signatures will support the development of accurate assays for the non-invasive detection and monitoring of BCa.
Project description:<h4>Background</h4>The recent discovery of miRNAs and lncRNAs in urine exosomes has emerged as promising diagnostic biomarkers for bladder cancer (BCa). However, mRNAs as the direct products of transcription has not been well evaluated in exosomes as biomarkers for BCa diagnosis. The purpose of this study was to identify tumor progression-related mRNAs and lncRNAs in urine exosomes that could be used for detection of BCa.<h4>Methods</h4>RNA-sequencing was performed to identify tumor progression-related biomarkers in three matched superficial tumor and deep infiltrating tumor regions of muscle-invasive bladder cancer (MIBC) specimens, differently expressed mRNAs and lncRNAs were validated in TCGA dataset (n = 391) in the discovery stage. Then candidate RNAs were chosen for evaluation in urine exosomes of a training cohort (10 BCa and 10 healthy controls) and a validation cohort (80 BCa and 80 healthy controls) using RT-qPCR. The diagnostic potential of the candidates were evaluated by receiver operating characteristic (ROC) curves.<h4>Results</h4>RNA sequencing revealed 8 mRNAs and 32 lncRNAs that were significantly upregulated in deep infiltrating tumor region. After validation in TCGA database, 10 markedly dysregulated RNAs were selected for further investigation in urine exosomes, of which five (mRNAs: KLHDC7B, CASP14, and PRSS1; lncRNAs: MIR205HG and GAS5) were verified to be significantly dysregulated. The combination of the five RNAs had the highest AUC to disguising the BCa (0.924, 95% CI, 0.875-0.974) or early stage BCa patients (0.910, 95% CI, 0.850 to 0.971) from HCs. The expression levels of these five RNAs were correlated with tumor stage, grade, and hematuria degrees.<h4>Conclusions</h4>These findings highlight the potential of urine exosomal mRNAs and lncRNAs profiling in the early diagnosis and provide new insights into the molecular mechanisms involved in BCa.
Project description:Urinary microRNAs (miRNAs) are emerging as clinically useful tool for early and non-invasive detection of various types of cancer including bladder cancer (BCA). In this study, 205 patients with BCA and 99 healthy controls were prospectively enrolled. Expression profiles of urinary miRNAs were obtained using Affymetrix miRNA microarrays (2578 miRNAs) and candidate miRNAs further validated in independent cohorts using qRT-PCR. Whole-genome profiling identified 76 miRNAs with significantly different concentrations in urine of BCA compared to controls (P < 0.01). In the training and independent validation phase of the study, miR-31-5p, miR-93-5p and miR-191-5p were confirmed to have significantly higher levels in urine of patients with BCA in comparison with controls (P < 0.01). We further established 2-miRNA-based urinary DxScore (miR-93-5p, miR-31-5p) enabling sensitive BCA detection with AUC being 0.84 and 0.81 in the training and validation phase, respectively. Moreover, DxScore significantly differed in the various histopathological subgroups of BCA and decreased post-operatively. In conclusion, we identified and independently validated cell-free urinary miRNAs as promising biomarkers enabling non-invasive detection of BCA.
Project description:Purpose. Nonmuscle invasive bladder cancer (BCa) has a high recurrence rate requiring lifelong surveillance. Urinary biomarkers are promising as simple alternatives to cystoscopy for the diagnosis of recurrent bladder cancer. However, no single marker can achieve the required accuracy. The purpose of this study was to select a multiparameter panel, comprising urinary biomarkers and clinical parameters, for BCa recurrence diagnosis. Experimental Design. Candidate biomarkers were measured in urine samples of BCa patients with recurrence and BCa patients without recurrence. A multiplatform strategy was used for marker quantification comprising a multiplexed microarray and an automated platform for ELISA analysis. A multivariate statistical analysis combined the results from both platforms with the collected clinical data. Results. The best performing combination of biomarkers and clinical parameters achieved an AUC value of 0.91, showing better performance than individual parameters. This panel comprises six biomarkers (cadherin-1, IL-8, ErbB2, IL-6, EN2, and VEGF-A) and three clinical parameters (number of past recurrences, number of BCG therapies, and stage at time of diagnosis). Conclusions. The multiparameter panel could be a useful noninvasive tool for BCa surveillance and potentially impact the clinical management of this disease. Validation of results in an independent cohort is warranted.
Project description:Currently, voided urine cytology (VUC) serves as the gold standard for the detection of bladder cancer (BCa) in urine. Despite its high specificity, VUC has shortcomings in terms of sensitivity. Therefore, alternative biomarkers are being searched, which might overcome these disadvantages as a useful adjunct to VUC. The aim of this study was to evaluate the diagnostic potential of the urinary levels of selected microRNAs (miRs), which might represent such alternative biomarkers due to their BCa-specific expression. Expression levels of nine BCa-associated microRNAs (miR-21, -96, -125b, -126, -145, -183, -205, -210, -221) were assessed by quantitative PCR in urine sediments from 104 patients with primary BCa and 46 control subjects. Receiver operating characteristic (ROC) curve analyses revealed a diagnostic potential for miR-96, -125b, -126, -145, -183, and -221 with area under the curve (AUC) values between 0.605 and 0.772. The combination of the four best candidates resulted in sensitivity, specificity, positive and negative predictive values (NPV), and accuracy of 73.1%, 95.7%, 97.4%, 61.1%, and 80.0%, respectively. Combined with VUC, sensitivity and NPV could be increased by nearly 8%, each surpassing the performance of VUC alone. The present findings suggested a diagnostic potential of miR-125b, -145, -183, and -221 in combination with VUC for non-invasive detection of BCa in urine.
Project description:The aim of the present study was to identify novel DNA methylation markers in bladder cancer (BCa) through genome-wide profiling of bladder cancer cell lines and subsequent MSP screening in urine samples. Experimental Design: MBD methylCap/seq was carried out to screen differentially methylated CpG islands using two BCa cell lines (5637 and T24) and two normal bladder mucosa (BM) samples. The top one hundred most hypermethylated targets were screened using Methylation Specific PCR (MSP) in small and big cohort of urine samples from BCa patients and normal controls. The diagnostic performance of the gene panel was further evaluated in different clinical scenarios. Results: In total, 1,627 gene promoter regions hypermethylated in BCa cell line were identified in genomic level methylation profiling. The followed screening procedure in clinical urine sample generated eight genes (VAX1, KCNV1, ECEL1, TMEM26, TAL1, PROX1, SLC6A20, and LMX1A) capable of differentiating BCa from normal control. Subsequent validation in a large sample size enabled the optimisation of 5 methylation targets (VAX1, KCNV1, TAL1, PPOX1 and CFTR) for BCa diagnosis with sensitivity and specificity of 86.32% and 87.13%, respectively. In addition, VAX1 and LMX1A methylation could predict the tumour recurrence. Conclusions: Tumor specific biomarkers of BCa could be established by first performing genome level methylation profiling with cell lines and then screening the potential targets in urine samples. The panel of methylated genes identified was promising for the early non-invasive detection and surveillance of BCa. MBD methylCap/seq was carried out to screen differentially methylated CpG islands using two BCa cell lines (5637 and T24), and two normal bladder tissue mix as control.
Project description:The aim of the present study was to identify novel DNA methylation markers in bladder cancer (BCa) through genome-wide profiling of bladder cancer cell lines and subsequent MSP screening in urine samples. Experimental Design: MBD methylCap/seq was carried out to screen differentially methylated CpG islands using two BCa cell lines (5637 and T24) and two normal bladder mucosa (BM) samples. The top one hundred most hypermethylated targets were screened using Methylation Specific PCR (MSP) in small and big cohort of urine samples from BCa patients and normal controls. The diagnostic performance of the gene panel was further evaluated in different clinical scenarios. Results: In total, 1,627 gene promoter regions hypermethylated in BCa cell line were identified in genomic level methylation profiling. The followed screening procedure in clinical urine sample generated eight genes (VAX1, KCNV1, ECEL1, TMEM26, TAL1, PROX1, SLC6A20, and LMX1A) capable of differentiating BCa from normal control. Subsequent validation in a large sample size enabled the optimisation of 5 methylation targets (VAX1, KCNV1, TAL1, PPOX1 and CFTR) for BCa diagnosis with sensitivity and specificity of 86.32% and 87.13%, respectively. In addition, VAX1 and LMX1A methylation could predict the tumour recurrence. Conclusions: Tumor specific biomarkers of BCa could be established by first performing genome level methylation profiling with cell lines and then screening the potential targets in urine samples. The panel of methylated genes identified was promising for the early non-invasive detection and surveillance of BCa. Overall design: MBD methylCap/seq was carried out to screen differentially methylated CpG islands using two BCa cell lines (5637 and T24), and two normal bladder tissue mix as control.
Project description:Bladder cancer (BCa) is a common malignancy worldwide and has a high probability of recurrence after initial diagnosis and treatment. As a result, recurrent surveillance, primarily involving repeated cystoscopies, is a critical component of post diagnosis patient management. Since cystoscopy is invasive, expensive and a possible deterrent to patient compliance with regular follow-up screening, new non-invasive technologies to aid in the detection of recurrent and/or primary bladder cancer are strongly needed. In this study, mass spectrometry based metabolomics was employed to identify biochemical signatures in human urine that differentiate bladder cancer from non-cancer controls. Over 1000 distinct compounds were measured including 587 named compounds of known chemical identity. Initial biomarker identification was conducted using a 332 subject sample set of retrospective urine samples (cohort 1), which included 66 BCa positive samples. A set of 25 candidate biomarkers was selected based on statistical significance, fold difference and metabolic pathway coverage. The 25 candidate biomarkers were tested against an independent urine sample set (cohort 2) using random forest analysis, with palmitoyl sphingomyelin, lactate, adenosine and succinate providing the strongest predictive power for differentiating cohort 2 cancer from non-cancer urines. Cohort 2 metabolite profiling revealed additional metabolites, including arachidonate, that were higher in cohort 2 cancer vs. non-cancer controls, but were below quantitation limits in the cohort 1 profiling. Metabolites related to lipid metabolism may be especially interesting biomarkers. The results suggest that urine metabolites may provide a much needed non-invasive adjunct diagnostic to cystoscopy for detection of bladder cancer and recurrent disease management.