BcCluster: A Bladder Cancer Database at the Molecular Level

Abstract

Background:Bladder Cancer (BC) has two clearly distinct phenotypes. Non-muscle invasive BC has good prognosis and is treated with tumor resection and intravesical therapy whereas muscle invasive BC has poor prognosis and requires usually systemic cisplatin based chemotherapy either prior to or after radical cystectomy. Neoadjuvant chemotherapy is not often used for patients undergoing cystectomy. High-throughput analytical omics techniques are now available that allow the identification of individual molecular signatures to characterize the invasive phenotype. However, a large amount of data produced by omics experiments is not easily accessible since it is often scattered over many publications or stored in supplementary files.Objective:To develop a novel open-source database, BcCluster (http://www.bccluster.org/), dedicated to the comprehensive molecular characterization of muscle invasive bladder carcinoma.Materials:A database was created containing all reported molecular features significant in invasive BC. The query interface was developed in Ruby programming language (version 1.9.3) using the web-framework Rails (version 4.1.5) (http://rubyonrails.org/).Results:BcCluster contains the data from 112 published references, providing 1,559 statistically significant features relative to BC invasion. The database also holds 435 protein-protein interaction data and 92 molecular pathways significant in BC invasion. The database can be used to retrieve binding partners and pathways for any protein of interest. We illustrate this possibility using survivin, a known BC biomarker.Conclusions:BcCluster is an online database for retrieving molecular signatures relative to BC invasion. This application offers a comprehensive view of BC invasiveness at the molecular level and allows formulation of research hypotheses relevant to this phenotype.

Integrative analysis of extracellular and intracellular bladder cancer cell line proteome with transcriptome: improving coverage and validity of –omics findings

“…Molecular features associated with BCa invasion or metastasis were retrieved using two independent approaches i.e. the BcCluster database 20 (n = 627) and the GLAD4U 21 tool (n = 671; Supplementary Dataset S3 ), as described in Methods. Validity was assessed based on the overlap between our experimental data and literature findings (listed in Supplementary Dataset S3 ).…”

“…As the first step in this direction, and to establish the relevance of each individual proteomics dataset, we evaluated our main findings in the context of the existing literature. We used a manually curated database of features (genes, transcripts, proteins) associated with BCa invasion/ progression (BcCluster) 20 . Importantly, BcCluster lists molecules highlighted from studies with sample size of at least 50, suggesting a high validity of the collected features.…”

“…An improvement in the agreement with existing literature data is observed (as described in Table 3 ), indicating the applicability and value of such a multi-omics approach to verify large scale proteomics data. Of these 253 features, 33 (13.0% BcCluster) 20 or 40 (15.8% Glad4U) 21 have been associated with BCa/ BCa invasion or metastasis. This corresponds to approximately a 5% increase in the overlapping features when compared with the respective overlap of all 614 differentially abundant proteins, identified across the three proteomics experiments.…”

“…Molecules (proteins and transcripts) associated with BCa invasion/ progression were retrieved from the BCa database ( http://bccluster.org/ ) 20 . GLAD4U 21 was also employed to retrieve relevant featured from MEDLINE database using the following keywords: (“bladder cancer” or “urothelial cancer” or “transitional cell carcinoma” or “urothelial cancer”) and (“invasion” or “progression” or “invasiveness” or “aggressiveness” or “metastasis”) with the undefined threshold settings for genes prioritization.…”

“…Total RNA sequencing analysis was utilized to complement and validate the large scale proteomic data sets. To assess the validity of findings in an unbiased way, these findings were compared to literature data represented by the BcCluster BCa database ( http://bccluster.org/ ) 20 and retrieved using the GLAD4U tool ( http://bioinfo.vanderbilt.edu/glad4u/ ) 21 . As shown, cross-strategy and –omics comparisons at the individual molecule and pathway levels increase the credibility of individual observations and improve proteome coverage consequently increasing data extraction from individual –omics experiments for further systems biology and/or targeted investigation.…”