Dataset Information

A gene expression ratio-based diagnostic test for bladder cancer.

ABSTRACT: PURPOSE:Bladder cancer is relatively common but early detection techniques such as cystoscopy and cytology are somewhat limited. We developed a broadly applicable, platform-independent and clinically relevant method based on simple ratios of gene expression to diagnose human cancers. In this study, we sought to determine whether this technique could be applied to the diagnosis of bladder cancer. EXPERIMENTAL DESIGN:We developed a model for the diagnosis of bladder cancer using expression profiling data from 80 normal and tumor bladder tissues to identify statistically significant discriminating genes with reciprocal average expression levels in each tissue type. The expression levels of select genes were used to calculate individual gene pair expression ratios in order to assign diagnosis. The optimal model was examined in two additional published microarray data sets and using quantitative RT-PCR in a cohort of 13 frozen benign bladder urothelium samples and 13 bladder cancer samples from our institution. RESULTS:A five-ratio test utilizing six genes proved to be 100% accurate (26 of 26 samples) for distinguishing benign from malignant bladder tissue samples (P < 10(-6)). CONCLUSIONS:: We have provided a proof of principle study for the use of gene expression ratios in the diagnosis of bladder cancer. This technique may ultimately prove to be a useful adjunct to cytopathology in screening urine specimens for bladder cancer.

SUBMITTER: Dong L

PROVIDER: S-EPMC3169945 | biostudies-literature | 2009

REPOSITORIES: biostudies-literature

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A gene expression ratio-based diagnostic test for bladder cancer.

Dong Lingsheng L Bard Andrew J AJ Richards William G WG Nitz Matthew D MD Theodorescu Dan D Bueno Raphael R Gordon Gavin J GJ

Advances and applications in bioinformatics and chemistry : AABC 20090112

<h4>Purpose</h4>Bladder cancer is relatively common but early detection techniques such as cystoscopy and cytology are somewhat limited. We developed a broadly applicable, platform-independent and clinically relevant method based on simple ratios of gene expression to diagnose human cancers. In this study, we sought to determine whether this technique could be applied to the diagnosis of bladder cancer.<h4>Experimental design</h4>We developed a model for the diagnosis of bladder cancer using exp ...[more]

PMID: 21918612

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Project description:BackgroundDue to insufficient accuracy, urine-based assays currently have a limited role in the management of patients with bladder cancer. The identification of multiplex molecular signatures associated with disease has the potential to address this deficiency and to assist with accurate, non-invasive diagnosis and monitoring.MethodsTo evaluate the performance of Oncuria™, a multiplex immunoassay for bladder detection in voided urine samples. The test was evaluated in a multi-institutional cohort of 362 prospectively collected subjects presenting for bladder cancer evaluation. The parallel measurement of 10 biomarkers (A1AT, APOE, ANG, CA9, IL8, MMP9, MMP10, PAI1, SDC1 and VEGFA) was performed in an independent clinical laboratory. The ability of the test to identify patients harboring bladder cancer was assessed. Bladder cancer status was confirmed by cystoscopy and tissue biopsy. The association of biomarkers and demographic factors was evaluated using linear discriminant analysis (LDA) and predictive models were derived using supervised learning and cross-validation analyses. Diagnostic performance was assessed using ROC curves.ResultsThe combination of the 10 biomarkers provided an AUROC 0.93 [95% CI 0.87-0.98], outperforming any single biomarker. The addition of demographic data (age, sex, and race) into a hybrid signature improved the diagnostic performance AUROC 0.95 [95% CI 0.90-1.00]. The hybrid signature achieved an overall sensitivity of 0.93, specificity of 0.93, PPV of 0.65 and NPV of 0.99 for bladder cancer classification. Sensitivity values of the diagnostic panel for high-grade bladder cancer, low-grade bladder cancer, MIBC and NMIBC were 0.94, 0.89, 0.97 and 0.93, respectively.ConclusionsUrinary levels of a biomarker panel enabled the accurate discrimination of bladder cancer patients and controls. The multiplex Oncuria™ test can achieve the efficient and accurate detection and monitoring of bladder cancer in a non-invasive patient setting.

Project description:BackgroundIn the past ~15 years, the identification of diagnostic and prognostic biomarkers from gene expression data has increased our understanding of cancer biology and has led to advances in the personalized treatment of many cancers. A diagnostic biomarker is indicative of tumor status such as tumor stage, while a prognostic biomarker is indicative of disease outcome. Despite these advances, however, there are no clinically approved biomarkers for the treatment of bladder cancer, which is the fourth most common cancer in males in the United States and one of the most expensive cancers to treat. Although gene expression profiles of bladder cancer patients are publicly available, biomarker identification requires bioinformatics expertise that is not available to many research laboratories.DescriptionWe collected gene expression data from 13 publicly available patient cohorts (N = 1454) and developed BC-BET, an online Bladder Cancer Biomarker Evaluation Tool for evaluating candidate diagnostic and prognostic gene expression biomarkers in bladder cancer. A user simply selects a gene, and BC-BET evaluates the utility of that gene's expression as a diagnostic and prognostic biomarker. Specifically, BC-BET calculates how strongly a gene's expression is associated with tumor presence (distinguishing tumor from normal samples), tumor grade (distinguishing low- from high-grade tumors), tumor stage (distinguishing non-muscle invasive from muscle invasive samples), and patient outcome (e.g., disease-specific survival) across all patients in each cohort. Patients with low-grade, non-muscle invasive tumors and patients with high-grade, muscle invasive tumors are also analyzed separately in order to evaluate whether the biomarker of interest has prognostic value independent of grade and stage.ConclusionAlthough bladder cancer gene expression datasets are publicly available, their analysis is computationally intensive and requires bioinformatics expertise. BC-BET is an easy-to-use tool for rapidly evaluating bladder cancer gene expression biomarkers across multiple patient cohorts.

Project description:BackgroundBladder cancer (BC) is the tenth most common cancer in the world. Serum microRNA (miRNA) profiles previously have been reported as non-invasive biomarkers in cancer screening. The non-invasive and reliable diagnostic biomarkers are urgently needed for detecting BC, while cystoscopy is invasive. Our study aimed to identify candidate miRNAs in serum as potential diagnostic biomarkers for BC detection.MethodsThis study was including the screening stage, training stage, and validation stage with 137 BC patients and 127 healthy controls (HCs). We identified the expression of 28 serum miRNAs from 5 BC pools and 3 HC pools in the initial screening stage. The other 112 BC patients and 112 HCs were randomly divided into training stage with 30 BC patients and 30 HCs and validation stages with 82 BC patients and 82 HCs. These HCs matched BC patients based on age and gender with P value >0.05. Identified dysregulated miRNAs were further confirmed in the training stage, and validation stages by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The diagnostic value of miRNAs was assessed by receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC). Target genes of 3 candidate miRNAs were predicted by bioinformatic analysis.ResultsFive miRNAs (miR-106a-5p, miR-145-5p, miR-132-3p, miR-7-5p and miR-148b-3p) in serum were obviously dysregulated in BC patients compared to HCs. The ability to diagnose BC of 3 candidate miRNAs was estimated by AUC, with miR-132-3p (AUC =0.781; sensitivity =68.29%, specificity =81.71%), miR-7-5p (AUC =0.778; sensitivity =59.76%, specificity =84.15%) and miR-148b-3p (AUC =0.837; sensitivity =81.71%, specificity =71.95%). Combined application of these candidate miRNAs with parallel test could improve the diagnostic value (AUC =0.922; sensitivity =90.24%, specificity =81.71%). BNC2, GAS7, and NTRK2, considered as target genes of the three-miRNA panel, may play an important role in the process of BC development.ConclusionsA three-miRNA panel in serum was identified for BC diagnosis in our study, which HCs were used for differential diagnosis. The three-miRNA panel (miR-132-3p, miR-7-5p, and miR-148b-3p) might be performed as a non-invasive and convenient diagnostic tool for BC screening and diagnosis.

Project description:Background and Objective: Bladder cancer is the most common tumor in the urinary system, with a higher incidence in men than in women and a high recurrence rate. However, the mechanism of recurrence is still unclear, and it is urgent to clarify the pathophysiological mechanism of bladder cancer. To provide theoretical basis for the development of new therapies, investigating the effect of tumor microenvironment on the prognosis of bladder cancer is necessary. Methods: We applied the Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm to the downloaded TCGA (The Cancer Genome Atlas) transcriptome data to obtain the immune scores and stromal scores of each sample, and then divided the samples into two groups: high and low immune scores (or high and low stromal scores), and found that some differential genes were associated with poor prognosis of patients. We then performed protein-protein interaction (PPI) network analysis to explore the relationship between these differentially expressed genes. Moreover, we also performed (Gene Ontology) GO and (Kyoto Encyclopedia of Genes and Genomes) KEGG analyses to explore the potential functions of differentially expressed genes. Finally, our results were validated in an independent dataset. Results: We identified 136 tumor microenvironment-related genes associated with poor prognosis of bladder cancer. GO annotation and KEGG pathway enrichment analysis found that these genes are mainly involved in extracellular matrix, Focal adhesion and phosphatidylinositol 3 kinase-protein kinaseB (PI3k-Akt) signaling pathway. Next, PPI network analysis revealed some hub genes including Versican (VCAN), Thrombospondin 1 (THBS1) and Thrombospondin 1 (THBS2). Finally, 27 genes were further verified in the independent data set. Conclusions: We found 27 tumor microenvironment-related genes of bladder cancer, which are associated with poor prognosis of bladder cancer. These genes may inspire researchers to develop new treatments for bladder cancer.

Dataset Information

A gene expression ratio-based diagnostic test for bladder cancer.

Publications

A gene expression ratio-based diagnostic test for bladder cancer.

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