We utilized targeted mass-spectrometry to study the metabolic fingerprint of urothelial cancer and in order to determine whether the biochemical pathway analysis gene signature would have a predictive value in independent bladder cancer patient cohorts.
PATIENT AND METHODS:
Pathologically evaluated bladder-derived tissues (benign adjacent n = 14 and bladder cancer n = 46) were analyzed using liquid chromatography based targeted mass spectrometry. Differential metabolites associated with tumors samples in comparison to benign tissue were identified by adjusting the p-values for multiple testing at an FDR threshold of 15%. Enrichment of pathways and processes associated with the metabolic signature were determined using Gene Ontology and Molecular Signature Databases. Integration of metabolite alterations with transcriptome data from TCGA was utilized to identify a molecular signature of 30 metabolic genes. Available outcome data from the TCGA portal was used to determine the association with survival.
We identified 145 metabolites in the analysis of which 31 metabolites were differential when comparing benign and tumor tissue samples. Using the KEGG Database we identified a total of 174 genes correlated with altered metabolic pathways involved. By integrating these genes with the transcriptomic data from the corresponding TCGA dataset we identified a metabolic signature consisting of 30 genes that was significant in its prediction of survival between patients with a low (n=95) versus a high signature score (n=282) (p = 0.0458).
Targeted mass spectrometry of bladder cancer is highly sensitive to detect metabolic alterations. Application of transcriptome data allows for integration into larger datasets and identification of relevant metabolic pathways in bladder cancer progression.