Integrative pathway analysis of metabolic signature in bladder cancer a Linkage to The Cancer Genome Atlas project and prediction of survival

Friedrich-Carl von Rundstedt, Kimal Rajapakshe, Jing Ma, James M. Arnold, Jie Gohlke, Vasanta Putluri, Rashmi Krishnapuram, D. Badrajee Piyarathna, Yair Lotan, Daniel Gödde, Stephan Roth, Stephan Störkel, Jonathan M. Levitt, George Michailidis, Arun Sreekumar, Seth P. Lerner, Cristian Coarfa, Nagireddy Putluri. (2016). The Journal of Urology
DOI
10.1016/j.juro.2016.01.039
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Abstract

Purpose

We used targeted mass spectrometry to study the metabolic fingerprint of urothelial cancer and determine whether the biochemical pathway analysis gene signature would have a predictive value in independent cohorts of patients with bladder cancer.

Materials and Methods

Pathologically evaluated, bladder derived tissues, including benign adjacent tissue from 14 patients and bladder cancer from 46, were analyzed by liquid chromatography based targeted mass spectrometry. Differential metabolites associated with tumor samples in comparison to benign tissue were identified by adjusting the p values for multiple testing at a false discovery rate threshold of 15%. Enrichment of pathways and processes associated with the metabolic signature were determined using the GO (Gene Ontology) Database and MSigDB (Molecular Signature Database). Integration of metabolite alterations with transcriptome data from TCGA (The Cancer Genome Atlas) was done to identify the molecular signature of 30 metabolic genes. Available outcome data from TCGA portal were used to determine the association with survival.

Results

We identified 145 metabolites, of which analysis revealed 31 differential metabolites when comparing benign and tumor tissue samples. Using the KEGG (Kyoto Encyclopedia of Genes and Genomes) Database we identified a total of 174 genes that correlated with the altered metabolic pathways involved. By integrating these genes with the transcriptomic data from the corresponding TCGA data set we identified a metabolic signature consisting of 30 genes. The signature was significant in its prediction of survival in 95 patients with a low signature score vs 282 with a high signature score (p = 0.0458).

Conclusions

Targeted mass spectrometry of bladder cancer is highly sensitive for detecting metabolic alterations. Applying transcriptome data allows for integration into larger data sets and identification of relevant metabolic pathways in bladder cancer progression.

Keywords urinary bladder neoplasms; urothelium; metabolomics; mass spectrometry; metabolic networks and pathways.