Skip to main content
Erschienen in: BMC Urology 1/2015

Open Access 01.12.2015 | Database

An online tool for evaluating diagnostic and prognostic gene expression biomarkers in bladder cancer

verfasst von: Garrett M. Dancik

Erschienen in: BMC Urology | Ausgabe 1/2015

Abstract

Background

In 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.

Description

We 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.

Conclusion

Although 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.
Begleitmaterial
Hinweise

Electronic supplementary material

The online version of this article (doi:10.​1186/​s12894-015-0056-z) contains supplementary material, which is available to authorized users.

Competing interests

The author declares that they have no competing interests.

Author contributions

GD conceived of, designed, and implemented the database, and wrote the manuscript.
Abkürzungen
LG
Low-grade
HG
High-grade
NMI
Non-muscle invasive bladder cancer
MI
Muscle invasive bladder cancer
FC
Fold-change
AUC
Area under the receiver operating characteristic curve
DSS
disease-specific survival
OS
Overall survival
RFS
Recurrence-free survival
HR
Hazard ratio

Background

Cancer is a genetic disease [1]. A cancer cell inherits or acquires mutations that enable it to grow efficiently, replicate indefinitely, support angiogenesis, avoid apoptosis, and in some cases metastasize [2]. In the past ~15 years, gene expression profiling of human cancers has revolutionized our understanding of cancer as a genetic disease and has expedited the identification of driver mutations and biomarkers for personalized treatment. A diagnostic biomarker is a molecule that is indicative of cancer status, such as the existence of a tumor, or its stage, grade, or clinical subtype; a prognostic biomarker is indicative of disease outcome.
Examples of prognostic biomarkers in routine clinical use include the OncotypeDx and MammaPrint gene panels, which both predict the likelihood of disease recurrence in breast cancer and provide patients and clinicians with relevant information regarding the potential benefit of chemotherapy [3, 4].
In the United States, bladder cancer is the fourth most common cancer in males, the eighth most common cancer in females [5], and one of the most expensive cancers to treat [6]. At diagnosis, approximately 20–30 % of bladder cancer patients harbor muscle invasive tumors [7] and these patients have a five-year survival rate of approximately 43 % [8]. However, despite the importance of this disease there are no prognostic biomarkers or targeted therapies in clinical use.
Data from high-throughput gene expression studies (that simultaneously measure the expression of 1000s of genes) are typically deposited into public databases such as the Gene Expression Omnibus (GEO; Barrett and Edgar, 2006) and ArrayExpress [9]. These databases function primarily as data repositories where data is downloaded and analyzed using in-house bioinformatics tools. This analysis is often time-consuming and requires computational resources and bioinformatics expertise often not available to biologists or clinician-researchers. Furthermore, there are currently no bladder cancer gene expression databases that allow for an automated and comprehensive evaluation of both diagnostic and prognostic biomarkers in patients across multiple cohorts. In particular, the GEO2R tool allows for the identification of diagnostic biomarkers in any GEO cohort, but cannot identify prognostic biomarkers and cannot analyze multiple patient cohorts in a single analysis. Other databases include the KM plotter [10], which generates Kaplan-Meier survival curves for lung, breast, and ovarian cancer patients only; and PrognoScan [11] and SurvExpress [12], which generates Kaplan-Meier curves for only a small subset of the available bladder cancer patients. None of the above mentioned tools can identify prognostic biomarkers independent of stage or grade.
Here, we describe an online Bladder Cancer Biomarker Evaluation Tool (BC-BET) for rapid evaluation of diagnostic and prognostic biomarkers in bladder cancer, using data from 13 patient cohorts (N = 1454). The next section describes the patient cohorts and analyses that are implemented in BC-BET. We then demonstrate the utility of the tool by analyzing FGFR3, a gene known to be associated with tumor grade and stage in bladder cancer.

Construction and content

Patient cohorts and gene expression datasets

We performed a systematic search of the Gene Expression Omnibus (GEO) [13] and Array Express [9] and identified all entries with the keyword “bladder cancer” whose data was obtained by “transcription profiling by array”. Cohorts were excluded if all patients received neoadjuvant or adjuvant chemotherapy. Of the patient cohorts identified, four cohorts (GSE88, GSE89, GSE7476, GSE27448) were then excluded due to an insufficient sample size for both diagnostic (<10 per group) and prognostic (<10 events) biomarker evaluation (see below). Eleven patient cohorts from GEO and ArrayExpress met our inclusion criteria. Two additional cohorts were identified following a literature search and downloaded from supplemental material to publication [14, 15]. In these cohorts, individual patients were excluded from the survival analyses if they were treated by neoadjuvant chemotherapy and excluded completely if their tumors did not have urothelial histology. Individual patients treated with intravesical therapy or adjuvant chemotherapy can be optionally excluded from the prognostic biomarker evaluation analysis (see Prognostic biomarker evaluation and survival analysis). In all, BC-BET contains 13 patient cohorts (N = 1454) profiled on 10 distinct microarray platforms. Cohorts are named according to the institution or individual responsible for collecting and uploading the dataset, and are summarized in Table 1. All patient samples were collected with approval from an appropriate Institutional Review Board, as documented in the original publications (see Table 1).
Table 1
The 13 patient cohorts (N = 1454) included in BC-BET. The numbers in the table correspond to the number of patients with each clinical characteristic or available endpoint that are included in the database and analyzed. A ‘-’ denotes insufficient sample size for analysis
  
# of samples
Cohort (availability)*
Platform
Normal, Tumor
LG, HG
NMI, MI
DSS
OS
RFS
Total (N)
AUH-1 [27] (GSE3167)
Affymetrix Human Genome U133A
9,41
8, 32
28, 13
50
AUH-2 [28]
(GSE5479)
MDL Human 3 k
98, 271
351, 51
404
Blaveri [14] (S)
UCSF Human Array 2.0
10, 68
27, 53
74
74
CNUH [29] (GSE13507)
Illumina human-6 v2.0
10, 165
105, 60
104, 61
165
165
175
DFCI [30] (GSE31684)
Affymetrix Human Genome U133 Plus 2.0
6, 84
15, 78
90
93
Lindgren [31] (GSE19915)
Swegene
12,144
72, 72
97, 45
142
156
Lindgren-2 [32] (GSE32548)
Illumina HumanHT-12 V3.0
56, 75
92, 38
89
131
MDA-1 [33] (GSE48276)
Illumina HumanHT-12 WG-DASL V4.0 R2
22
22
MDA-2 [33] (GSE48075)
Illumina HumanHT-12 V3.0
67, 73
73
140
MSKCC [15] (S)
Affymetrix Human Genome U133A
38,91
18, 73
25, 66
87
129
UVA [34] (GSE37317)
Affymetrix Human Genome U133A
8, 10
18
Stransky-1 [35] (E-TABM-147)
Affymetrix Human Genome U95A
5,26
11, 15
9, 17
31
Stransky-2 [35] (E-TABM-147)
Affymetrix Human Genome U95Av2
13, 16
16, 15
31
Total
 
74, 467
397, 769
839, 523
252
565
90
1454
*Gene expression data for all cohorts are publicly available from the Gene Expression Omnibus (GEO) [13] with the given Accession # (GSE ID), from Array Express [9] (Accession # E-TABM-147) or as Supplementary material to publication (S). patients have MI, HG tumors (MDA-1) or MI tumors with unspecified grade (MDA-2). Abbreviations: LG, low grade; HG, high grade; NMI, non-muscle invasive; MI, muscle-invasive; DSS, disease-specific survival; OS, overall survival; RFS, recurrence-free survival
For each cohort, the processed gene expression data was downloaded. Three cohorts (Blaveri, AUH-2, and MDA-1) had missing values. Microarray probes with missing values in >20 % of samples were removed and expression values imputed using the impute package (impute.knn function) in R with default parameters. Probes are matched to genes based on current microarray (e.g., Affymetrix) annotation. When multiple probes exist for a gene, the probe with the highest mean expression is used [16]. In cohorts with replicate samples (AUH-2, MSKCC, Stransky-1, and Stransky-2), replicate samples were averaged to produce a single gene expression profile for each patient. In the MDA-1 cohort, because only two individuals had NMI tumors, the two individuals with NMI tumors are excluded from the database. When the same patient was profiled in multiple cohorts, and both cohorts are analyzed (i.e., the gene of interest is profiled in both cohorts), then duplicate patients are removed from one of the cohorts in order to prevent biasing of the results. For details, and for more information about the patient cohorts, sample processing, and patient exclusion, see Additional file 1: Table S1.

Diagnostic biomarker evaluation

For a specified gene, BC-BET evaluates whether the candidate gene is differentially expressed between the following groups which we denote as group A and group B, respectively: tumor and normal samples; high-grade (HG) and low-grade (LG) tumors; and muscle invasive (MI) and non-muscle invasive (NMI) tumors. LG and HG tumors correspond to G1-G2 and G3, respectively, or are classified according to the low- vs. high-grade classification system [17, 18]. NMI and MI tumors correspond to Ta-T1 or T2-T4 tumors, respectively [19]. Group differences are quantified by fold-change (FC) or by the area under the receiver operating characteristics curve (AUC). FC is calculated using the formula
$$ FC=\frac{mean\ expression\ of\ group\ A\ samples}{mean\ expression\ of\ group\ B\ samples} $$
The AUC quantifies the ability of gene expression to discriminate between two groups and is equivalent to the probability that a randomly selected sample from group A has higher expression than a randomly selected sample from group B. We note that AUC is a better measure of a biomarker’s predictive value, while FC may have a more intuitive biological interpretation. Statistical significance is assessed by either the two-sample t-test (which tests the hypothesis that FC = 1) or the non-parametric Wilcoxon-Rank sum test (which tests the hypothesis that AUC = 0.50).

Prognostic biomarker evaluation and survival analysis

Endpoints include disease-specific survival (DSS), overall survival (OS), or recurrence-free survival (RFS). The events for these endpoints are death from disease, death from any cause, and disease recurrence for DSS, OS, and RFS, respectively. Because there is no single endpoint consistently available across all cohorts with survival information, a user can opt to use the Best Available endpoint, which is taken to be the first available endpoint in the following order: DSS, OS, and RFS. The endpoints available for each cohort are listed in Table 1. Although selection of the Best Available endpoint results in the selection of different endpoints for different cohorts, patients with MI tumors have significantly (P < 0.05) poorer outcomes than patients with NMI tumors using the best available endpoint in 5/6 cohorts that include both NMI and MI tumors (Additional file 1: Table S2), which is consistent with RFS and OS analyses in a long-term study of over one thousand patients [8]. Patients with HG tumors also have significantly (P < 0.05) poorer outcomes than patients with LG tumors in all cohorts with more than 10 NMI tumors (Additional file 1: Table S2). Therefore, stage and grade are consistently associated with outcome in these diverse cohorts when the best available endpoint is used.
With the exception of CNUH and DFCI, definitive treatment of all patients was either transurethral resection of the bladder or radical cystectomy. In CNUH, 56 patients with NMI tumors received intravesical Bacillus Calmette-Guerin therapy and 26 patients with MI tumors received cisplatin-based adjuvant chemotherapy (along with one patient with an NMI tumor). Because neither of these treatments are associated with outcome in this cohort (Additional file 2: Figure S1A-B), these patients are included by default in the survival analysis, but optionally can be excluded. In DFCI, there is a confounding between TNM staging and treatment with chemotherapy. In particular, 32 patients with MI tumors receive adjuvant chemotherapy while 1 patient with an NMI tumor receives adjuvant chemotherapy. Of the treated patients having MI tumors, 84 % have nodal involvement or distant metastases, and treated patients have a higher risk of recurrence than non-treated patients (Additional file 2: Figure S1C). The user can optionally remove these treated patients from the survival analysis if desired.
Survival analysis is carried out using the coxph function in R to obtain a hazard ratio (HR) and log-rank p-value based on the cutpoint selected by the user. If the median cutpoint is selected, patients are separated into two groups according to those with high expression (≥ the median) and low expression (< the median), and the HR corresponds to the hazard rate of the high expressers relative to the hazard rate of the low expressers. Alternatively, the continuous expression value of the gene may be used, in which case the HR is relative to a two-fold increase in gene expression.

Utility and discussion

BC-BET was developed to allow for a systematic and comprehensive evaluation of candidate diagnostic and prognostic biomarkers across publicly available bladder cancer gene expression datasets. The user selects a gene, and BC-BET evaluates whether the gene of interest is a robust biomarker for tumor status, grade, and stage, based on its expression. For diagnostic biomarkers, the user selects the class comparison measure (FC or AUC) to quantify differences between tumor and normal samples, HG and LG samples, and MI and NMI samples.
To evaluate the utility of a gene as a prognostic biomarker, the user selects the desired endpoint (DSS, OS, RFS, or Best Available; see Construction and Content). Three analyses are then carried out: an evaluation of the prognostic value of the gene in cohorts containing patients with both NMI and MI tumors, in patients containing only LG, NMI tumors in each cohort, and in patients containing only HG, MI tumors. The latter analyses are critical for evaluating whether a biomarker has prognostic value independent of stage and grade, and are a unique feature of BC-BET. We note that there is one cohort (MDA-2) containing patients with MI tumors and unknown grade status, and the results from this cohort are displayed along with the results for patients with HG, MI tumors.

Case study: evaluation of FGFR3

In order to demonstrate the functionality of BC-BET, we will evaluate the diagnostic and prognostic value of fibroblast growth factor receptor 3 (FGFR3). Genomic studies in bladder cancer suggest that MI and NMI tumors arise through divergent genomic pathways [20] and may arise from distinct progenitor cell types [21]. Specifically, activating HRAS and TP53/RB1 mutations are more prevalent in MI tumors while FGFR3 and KDM6A mutations are more prevalent in NMI and LG tumors [2226]. Furthermore, FGFR3 mutation status is positively correlated with FGFR3 expression [25]. We therefore expect high FGFR3 mRNA expression to be associated with NMI and LG tumors in BC-BET.
A screenshot of the BC-BET homepage is provided in Fig. 1. Here the user selects the gene symbol of interest from a drop down menu of available genes, and sets the statistical parameters for the analysis. For class comparison, we will use FC to measure differential expression across tumor status, grade, and stage, and calculate p-values using the Wilcoxon Rank-Sum test. For survival analysis, we will look at continuous FGFR3 expression using the Best Available endpoint. The user also has the option of including or excluding treated patients from the CNUH and DFCI cohorts. Clicking on the Patient Analysis button carries out the desired evaluation.
Figure 2 contains a screenshot summarizing the diagnostic (Fig. 2a) and prognostic (Fig. 2b) value of FGFR3. The diagnostic value of the gene is summarized and illustrated according to whether the gene is up-regulated (FC > 1) or down-regulated (FC < 1) in group A (i.e., tumor samples, HG tumors, or MI tumors) relative to group B (i.e., normal samples, LG tumors, or NMI tumors, respectively), and whether or not the result is statistically significant (P < 0.05). The prognostic value of the gene is summarized according to whether the gene is negatively (HR > 1) or positively (HR < 1) associated with survival, and whether or not the result is statistically significant (P < 0.05). A legend is included on the results page displayed by BC-BET (Fig. 2c).
Clicking on the appropriate View Results radio button provides detailed numeric results indicating the diagnostic value (FC and p-value) and prognostic value (HR and logrank p-value) for each cohort for the chosen analysis. By default, the “Tumor vs. Normal” and “Poor vs. Good Prognosis (NMI + MI patients)” detailed results are shown. The full set of results can be downloaded as an Excel spreadsheet by clicking the Download Results link at the top of the page (Additional file 3: Table S3).
From these results we see that FGFR3 expression is significantly (P < 0.05) up-regulated in tumor samples (i.e., is higher in tumor samples than in normal samples) in 4/5 cohorts with this information, indicating its value as a diagnostic biomarker. FGFR3 expression is also significantly (P < 0.05) down-regulated in HG tumors (i.e., is higher in LG tumors than HG tumors) in 8/10 cohorts, and significantly down-regulated in MI tumors in 11/12 cohorts with stage information. However, as a prognostic biomarker, FGFR3 expression is only significantly (P < 0.05) associated with outcome in 2/6 cohorts, although in all cohorts it is positively associated with survival (HR < 1). In addition, FGFR3 does not have any prognostic value independent of stage and grade, as it is not significantly associated with outcome in patients with LG, NMI tumors or consistently associated with outcome in patients with HG, MI tumors. The above results are consistent with previous studies showing that FGFR3 protein expression is associated with stage and grade in bladder cancer [25], and supports investigation into targeting FGFR3 in patients with NMI tumors [20]. However, FGFR3 expression has little prognostic value based on the available gene expression cohorts.

Future developments

BC-BET will be updated periodically with additional gene expression datasets as they become available. In addition, new features including the ability to query the database with a list of genes or by probe, and the option to generate Kaplan-Meier curves are expected to be implemented in future versions of BC-BET.

Conclusions

The identification of diagnostic and prognostic biomarkers based on gene expression data is a powerful approach for investigating cancer. Although many gene expression datasets are available, the comprehensive analysis of multiple patient cohorts requires bioinformatics expertise not always available to researchers. BC-BET is a Bladder Cancer Biomarker Evaluation Tool developed for the comprehensive and rapid evaluation of diagnostic and prognostic biomarkers in bladder cancer across multiple patient cohorts. However, BC-BET has several limitations. In particular, the available patient cohorts included here (Table 1 and Additional file 1: Table S1) are heterogeneous, profiled on multiple platforms, and lack a common endpoint for survival analyses. Although the identification of diagnostic or prognostic biomarkers in multiple patient cohorts is promising, any biomarker identified using BC-BET ultimately must be prospectively validated to determine its clinical utility. Nevertheless, the identification of robust biomarkers in bladder cancer will increase our understanding of this disease and may have important implications for treatment.

Availability and requirements

BC-BET requires only a web browser and is available from Eastern Connecticut State University’s Bioinformatics Page: http://​bioinformatics.​easternct.​edu/​BCBET.

Acknowledgements

The author thanks Yuanbin Ru for critically reading the manuscript. This work was supported, in part, by a grant from the American Association of University Professors and Connecticut State University Board of Regents (DANR14).
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​4.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://​creativecommons.​org/​publicdomain/​zero/​1.​0/​) applies to the data made available in this article, unless otherwise stated.

Competing interests

The author declares that they have no competing interests.

Author contributions

GD conceived of, designed, and implemented the database, and wrote the manuscript.
Anhänge

Aditional files

Literatur
2.
Zurück zum Zitat Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74.CrossRefPubMed Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell. 2011;144(5):646–74.CrossRefPubMed
3.
Zurück zum Zitat Knauer M, Straver M, Rutgers E, Bender R, Cardoso F, Mook S, et al. The 70-gene MammaPrint signature is predictive for chemotherapy benefit in early breast cancer. Breast. 2009;18:S36–7. Knauer M, Straver M, Rutgers E, Bender R, Cardoso F, Mook S, et al. The 70-gene MammaPrint signature is predictive for chemotherapy benefit in early breast cancer. Breast. 2009;18:S36–7.
4.
Zurück zum Zitat Markopoulos C, Xepapadakis G, Venizelos V, Tsiftsoglou A, Misitzis J, Panoussis D, et al. Clinical Use of OncotypeDX recurrence score as an adjuvant-treatment decision tool in early breast cancer patients. Eur J Cancer. 2011;47:S379.CrossRef Markopoulos C, Xepapadakis G, Venizelos V, Tsiftsoglou A, Misitzis J, Panoussis D, et al. Clinical Use of OncotypeDX recurrence score as an adjuvant-treatment decision tool in early breast cancer patients. Eur J Cancer. 2011;47:S379.CrossRef
5.
Zurück zum Zitat Siegel R, Naishadham D, Jemal A. Cancer statistics, 2012. CA Cancer J Clin. 2012;62(1):10–29.CrossRefPubMed Siegel R, Naishadham D, Jemal A. Cancer statistics, 2012. CA Cancer J Clin. 2012;62(1):10–29.CrossRefPubMed
6.
Zurück zum Zitat Botteman MF, Pashos CL, Redaelli A, Laskin B, Hauser R. The health economics of bladder cancer: a comprehensive review of the published literature. Pharmacoeconomics. 2003;21(18):1315–30.CrossRefPubMed Botteman MF, Pashos CL, Redaelli A, Laskin B, Hauser R. The health economics of bladder cancer: a comprehensive review of the published literature. Pharmacoeconomics. 2003;21(18):1315–30.CrossRefPubMed
7.
Zurück zum Zitat Jacobs BL, Lee CT, Montie JE. Bladder cancer in 2010: how far have we come? CA Cancer J Clin. 2010;60(4):244–72.CrossRefPubMed Jacobs BL, Lee CT, Montie JE. Bladder cancer in 2010: how far have we come? CA Cancer J Clin. 2010;60(4):244–72.CrossRefPubMed
8.
Zurück zum Zitat Stein JP, Lieskovsky G, Cote R, Groshen S, Feng AC, Boyd S, et al. Radical cystectomy in the treatment of invasive bladder cancer: long-term results in 1,054 patients. J Clin Oncol. 2001;19(3):666–75.PubMed Stein JP, Lieskovsky G, Cote R, Groshen S, Feng AC, Boyd S, et al. Radical cystectomy in the treatment of invasive bladder cancer: long-term results in 1,054 patients. J Clin Oncol. 2001;19(3):666–75.PubMed
9.
Zurück zum Zitat Rustici G, Kolesnikov N, Brandizi M, Burdett T, Dylag M, Emam I, et al. ArrayExpress update–trends in database growth and links to data analysis tools. Nucleic Acids Res. 2013;41(Database issue):D987–90.CrossRefPubMed Rustici G, Kolesnikov N, Brandizi M, Burdett T, Dylag M, Emam I, et al. ArrayExpress update–trends in database growth and links to data analysis tools. Nucleic Acids Res. 2013;41(Database issue):D987–90.CrossRefPubMed
10.
Zurück zum Zitat Gyorffy B, Lanczky A, Eklund AC, Denkert C, Budczies J, Li Q, et al. An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Res Treat. 2010;123(3):725–31.CrossRefPubMed Gyorffy B, Lanczky A, Eklund AC, Denkert C, Budczies J, Li Q, et al. An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Res Treat. 2010;123(3):725–31.CrossRefPubMed
11.
Zurück zum Zitat Mizuno H, Kitada K, Nakai K, Sarai A. PrognoScan: a new database for meta-analysis of the prognostic value of genes. BMC Med Genet. 2009;2:18. Mizuno H, Kitada K, Nakai K, Sarai A. PrognoScan: a new database for meta-analysis of the prognostic value of genes. BMC Med Genet. 2009;2:18.
12.
Zurück zum Zitat Aguirre-Gamboa R, Gomez-Rueda H, Martinez-Ledesma E, Martinez-Torteya A, Chacolla-Huaringa R, Rodriguez-Barrientos A, et al. SurvExpress: an online biomarker validation tool and database for cancer gene expression data using survival analysis. PLoS One. 2013;8(9):e74250.CrossRefPubMedPubMedCentral Aguirre-Gamboa R, Gomez-Rueda H, Martinez-Ledesma E, Martinez-Torteya A, Chacolla-Huaringa R, Rodriguez-Barrientos A, et al. SurvExpress: an online biomarker validation tool and database for cancer gene expression data using survival analysis. PLoS One. 2013;8(9):e74250.CrossRefPubMedPubMedCentral
13.
Zurück zum Zitat Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, et al. NCBI GEO: archive for functional genomics data sets–10 years on. Nucleic Acids Res. 2011;39(Database issue):D1005–10.CrossRefPubMed Barrett T, Troup DB, Wilhite SE, Ledoux P, Evangelista C, Kim IF, et al. NCBI GEO: archive for functional genomics data sets–10 years on. Nucleic Acids Res. 2011;39(Database issue):D1005–10.CrossRefPubMed
14.
Zurück zum Zitat Blaveri E, Simko JP, Korkola JE, Brewer JL, Baehner F, Mehta K, et al. Bladder cancer outcome and subtype classification by gene expression. Clin Cancer Res Off J Am Assoc Cancer Res. 2005;11(11):4044–55.CrossRef Blaveri E, Simko JP, Korkola JE, Brewer JL, Baehner F, Mehta K, et al. Bladder cancer outcome and subtype classification by gene expression. Clin Cancer Res Off J Am Assoc Cancer Res. 2005;11(11):4044–55.CrossRef
15.
Zurück zum Zitat Sanchez-Carbayo M, Socci ND, Lozano J, Saint F, Cordon-Cardo C. Defining molecular profiles of poor outcome in patients with invasive bladder cancer using oligonucleotide microarrays. J Clin Oncol. 2006;24(5):778–89.CrossRefPubMed Sanchez-Carbayo M, Socci ND, Lozano J, Saint F, Cordon-Cardo C. Defining molecular profiles of poor outcome in patients with invasive bladder cancer using oligonucleotide microarrays. J Clin Oncol. 2006;24(5):778–89.CrossRefPubMed
16.
Zurück zum Zitat Miller JA, Cai C, Langfelder P, Geschwind DH, Kurian SM, Salomon DR, et al. Strategies for aggregating gene expression data: the collapseRows R function. BMC Bioinformatics. 2011;12:322.CrossRefPubMedPubMedCentral Miller JA, Cai C, Langfelder P, Geschwind DH, Kurian SM, Salomon DR, et al. Strategies for aggregating gene expression data: the collapseRows R function. BMC Bioinformatics. 2011;12:322.CrossRefPubMedPubMedCentral
17.
Zurück zum Zitat Mostofi F, Sobin L, Tosoni I. Histological typing of urinary bladder tumours. International histological classification of tumours, No 19. Geneva: World Health Organization; 1973. Mostofi F, Sobin L, Tosoni I. Histological typing of urinary bladder tumours. International histological classification of tumours, No 19. Geneva: World Health Organization; 1973.
18.
Zurück zum Zitat The International Agency for Research on Cancer. Non-invasive urothelial tumors. In: Eble JN, Epstein JI, Sesterhenn IA, Sauter G, editors. Pathology and genetics of tumours of the urinary system and male genital organs (IARC WHO classification of tumours). Lyon, France: IARC Press; 2004. The International Agency for Research on Cancer. Non-invasive urothelial tumors. In: Eble JN, Epstein JI, Sesterhenn IA, Sauter G, editors. Pathology and genetics of tumours of the urinary system and male genital organs (IARC WHO classification of tumours). Lyon, France: IARC Press; 2004.
19.
Zurück zum Zitat Edge SB, American Joint Committee on Cancer., American Cancer Society. AJCC cancer staging handbook : from the AJCC cancer staging manual, 7th edn. New York: Springer; 2010. Edge SB, American Joint Committee on Cancer., American Cancer Society. AJCC cancer staging handbook : from the AJCC cancer staging manual, 7th edn. New York: Springer; 2010.
20.
Zurück zum Zitat Wu XR. Urothelial tumorigenesis: a tale of divergent pathways. Nat Rev Cancer. 2005;5(9):713–25.CrossRefPubMed Wu XR. Urothelial tumorigenesis: a tale of divergent pathways. Nat Rev Cancer. 2005;5(9):713–25.CrossRefPubMed
21.
Zurück zum Zitat Dancik GM, Owens CR, Iczkowski KA, Theodorescu D. A cell of origin gene signature indicates human bladder cancer has distinct cellular progenitors. Stem Cells. 2014;32(4):974–82.CrossRefPubMedPubMedCentral Dancik GM, Owens CR, Iczkowski KA, Theodorescu D. A cell of origin gene signature indicates human bladder cancer has distinct cellular progenitors. Stem Cells. 2014;32(4):974–82.CrossRefPubMedPubMedCentral
22.
Zurück zum Zitat Bakkar AA, Wallerand H, Radvanyi F, Lahaye JB, Pissard S, Lecerf L, et al. FGFR3 and TP53 gene mutations define two distinct pathways in urothelial cell carcinoma of the bladder. Cancer Res. 2003;63(23):8108–12.PubMed Bakkar AA, Wallerand H, Radvanyi F, Lahaye JB, Pissard S, Lecerf L, et al. FGFR3 and TP53 gene mutations define two distinct pathways in urothelial cell carcinoma of the bladder. Cancer Res. 2003;63(23):8108–12.PubMed
23.
Zurück zum Zitat Gui Y, Guo G, Huang Y, Hu X, Tang A, Gao S, et al. Frequent mutations of chromatin remodeling genes in transitional cell carcinoma of the bladder. Nat Genet. 2011;43(9):875–8.CrossRefPubMed Gui Y, Guo G, Huang Y, Hu X, Tang A, Gao S, et al. Frequent mutations of chromatin remodeling genes in transitional cell carcinoma of the bladder. Nat Genet. 2011;43(9):875–8.CrossRefPubMed
24.
25.
Zurück zum Zitat Tomlinson DC, Baldo O, Harnden P, Knowles MA. FGFR3 protein expression and its relationship to mutation status and prognostic variables in bladder cancer. J Pathol. 2007;213(1):91–8.CrossRefPubMedPubMedCentral Tomlinson DC, Baldo O, Harnden P, Knowles MA. FGFR3 protein expression and its relationship to mutation status and prognostic variables in bladder cancer. J Pathol. 2007;213(1):91–8.CrossRefPubMedPubMedCentral
26.
Zurück zum Zitat van Rhijn BW, van der Kwast TH, Vis AN, Kirkels WJ, Boeve ER, Jobsis AC, et al. FGFR3 and P53 characterize alternative genetic pathways in the pathogenesis of urothelial cell carcinoma. Cancer Res. 2004;64(6):1911–4.CrossRefPubMed van Rhijn BW, van der Kwast TH, Vis AN, Kirkels WJ, Boeve ER, Jobsis AC, et al. FGFR3 and P53 characterize alternative genetic pathways in the pathogenesis of urothelial cell carcinoma. Cancer Res. 2004;64(6):1911–4.CrossRefPubMed
27.
Zurück zum Zitat Dyrskjot L, Kruhoffer M, Thykjaer T, Marcussen N, Jensen JL, Moller K, et al. Gene expression in the urinary bladder: a common carcinoma in situ gene expression signature exists disregarding histopathological classification. Cancer Res. 2004;64(11):4040–8.CrossRefPubMed Dyrskjot L, Kruhoffer M, Thykjaer T, Marcussen N, Jensen JL, Moller K, et al. Gene expression in the urinary bladder: a common carcinoma in situ gene expression signature exists disregarding histopathological classification. Cancer Res. 2004;64(11):4040–8.CrossRefPubMed
28.
Zurück zum Zitat Dyrskjot L, Zieger K, Real FX, Malats N, Carrato A, Hurst C, et al. Gene expression signatures predict outcome in non-muscle-invasive bladder carcinoma: a multicenter validation study. Clin Cancer Res Off J Am Assoc Cancer Res. 2007;13(12):3545–51.CrossRef Dyrskjot L, Zieger K, Real FX, Malats N, Carrato A, Hurst C, et al. Gene expression signatures predict outcome in non-muscle-invasive bladder carcinoma: a multicenter validation study. Clin Cancer Res Off J Am Assoc Cancer Res. 2007;13(12):3545–51.CrossRef
29.
Zurück zum Zitat Kim WJ, Kim EJ, Kim SK, Kim YJ, Ha YS, Jeong P, et al. Predictive value of progression-related gene classifier in primary non-muscle invasive bladder cancer. Mol Cancer. 2010;9:3.CrossRefPubMedPubMedCentral Kim WJ, Kim EJ, Kim SK, Kim YJ, Ha YS, Jeong P, et al. Predictive value of progression-related gene classifier in primary non-muscle invasive bladder cancer. Mol Cancer. 2010;9:3.CrossRefPubMedPubMedCentral
30.
Zurück zum Zitat Riester M, Taylor JM, Feifer A, Koppie T, Rosenberg JE, Downey RJ, et al. Combination of a novel gene expression signature with a clinical nomogram improves the prediction of survival in high-risk bladder cancer. Clin Cancer Res Off J Am Assoc Cancer Res. 2012;18(5):1323–33.CrossRef Riester M, Taylor JM, Feifer A, Koppie T, Rosenberg JE, Downey RJ, et al. Combination of a novel gene expression signature with a clinical nomogram improves the prediction of survival in high-risk bladder cancer. Clin Cancer Res Off J Am Assoc Cancer Res. 2012;18(5):1323–33.CrossRef
31.
Zurück zum Zitat Lindgren D, Frigyesi A, Gudjonsson S, Sjodahl G, Hallden C, Chebil G, et al. Combined gene expression and genomic profiling define two intrinsic molecular subtypes of urothelial carcinoma and gene signatures for molecular grading and outcome. Cancer Res. 2010;70(9):3463–72.CrossRefPubMed Lindgren D, Frigyesi A, Gudjonsson S, Sjodahl G, Hallden C, Chebil G, et al. Combined gene expression and genomic profiling define two intrinsic molecular subtypes of urothelial carcinoma and gene signatures for molecular grading and outcome. Cancer Res. 2010;70(9):3463–72.CrossRefPubMed
32.
Zurück zum Zitat Lindgren D, Sjodahl G, Lauss M, Staaf J, Chebil G, Lovgren K, et al. Integrated genomic and gene expression profiling identifies two major genomic circuits in urothelial carcinoma. PLoS One. 2012;7(6):e38863.CrossRefPubMedPubMedCentral Lindgren D, Sjodahl G, Lauss M, Staaf J, Chebil G, Lovgren K, et al. Integrated genomic and gene expression profiling identifies two major genomic circuits in urothelial carcinoma. PLoS One. 2012;7(6):e38863.CrossRefPubMedPubMedCentral
33.
Zurück zum Zitat Choi W, Porten S, Kim S, Willis D, Plimack ER, Hoffman-Censits J, et al. Identification of distinct basal and luminal subtypes of muscle-invasive bladder cancer with different sensitivities to frontline chemotherapy. Cancer Cell. 2014;25(2):152–65.CrossRefPubMedPubMedCentral Choi W, Porten S, Kim S, Willis D, Plimack ER, Hoffman-Censits J, et al. Identification of distinct basal and luminal subtypes of muscle-invasive bladder cancer with different sensitivities to frontline chemotherapy. Cancer Cell. 2014;25(2):152–65.CrossRefPubMedPubMedCentral
34.
Zurück zum Zitat Smith SC, Baras AS, Owens CR, Dancik G, Theodorescu D. Transcriptional signatures of Ral GTPase are associated with aggressive clinicopathologic characteristics in human cancer. Cancer Res. 2012;72(14):3480–91.CrossRefPubMed Smith SC, Baras AS, Owens CR, Dancik G, Theodorescu D. Transcriptional signatures of Ral GTPase are associated with aggressive clinicopathologic characteristics in human cancer. Cancer Res. 2012;72(14):3480–91.CrossRefPubMed
35.
Zurück zum Zitat Stransky N, Vallot C, Reyal F, Bernard-Pierrot I, de Medina SG, Segraves R, et al. Regional copy number-independent deregulation of transcription in cancer. Nat Genet. 2006;38(12):1386–96.CrossRefPubMed Stransky N, Vallot C, Reyal F, Bernard-Pierrot I, de Medina SG, Segraves R, et al. Regional copy number-independent deregulation of transcription in cancer. Nat Genet. 2006;38(12):1386–96.CrossRefPubMed
36.
Zurück zum Zitat Smith SC, Baras AS, Dancik G, Ru Y, Ding KF, Moskaluk CA, et al. A 20-gene model for molecular nodal staging of bladder cancer: development and prospective assessment. Lancet Oncol. 2011;12(2):137–43.CrossRefPubMedPubMedCentral Smith SC, Baras AS, Dancik G, Ru Y, Ding KF, Moskaluk CA, et al. A 20-gene model for molecular nodal staging of bladder cancer: development and prospective assessment. Lancet Oncol. 2011;12(2):137–43.CrossRefPubMedPubMedCentral
Metadaten
Titel
An online tool for evaluating diagnostic and prognostic gene expression biomarkers in bladder cancer
verfasst von
Garrett M. Dancik
Publikationsdatum
01.12.2015
Verlag
BioMed Central
Erschienen in
BMC Urology / Ausgabe 1/2015
Elektronische ISSN: 1471-2490
DOI
https://doi.org/10.1186/s12894-015-0056-z

Weitere Artikel der Ausgabe 1/2015

BMC Urology 1/2015 Zur Ausgabe

Update Urologie

Bestellen Sie unseren Fach-Newsletter und bleiben Sie gut informiert.