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J Urol. 2016 Jun;195(6):1911-9. doi: 10.1016/j.juro.2016.01.039. Epub 2016 Jan 21.

Integrative Pathway Analysis of Metabolic Signature in Bladder Cancer: A Linkage to The Cancer Genome Atlas Project and Prediction of Survival.

Author information

1
Scott Department of Urology, Baylor College of Medicine, Houston, Texas; Department of Urology, Witten-Herdecke University, Wuppertal, Germany.
2
Department of Molecular and Cell Biology, Alkek Center for Molecular Discovery, Baylor College of Medicine, Houston, Texas.
3
Department of Statistics, University of Michigan, Ann Arbor, Michigan.
4
Advanced Technology Core, Baylor College of Medicine, Houston, Texas.
5
Department of Molecular and Cell Biology, Alkek Center for Molecular Discovery, Baylor College of Medicine, Houston, Texas; Advanced Technology Core, Baylor College of Medicine, Houston, Texas.
6
Department of Urology, University of Texas Southwestern Medical Center, Dallas, Texas.
7
Department of Pathology Helios Klinikum, Witten-Herdecke University, Wuppertal, Germany.
8
Department of Urology, Witten-Herdecke University, Wuppertal, Germany.
9
Scott Department of Urology, Baylor College of Medicine, Houston, Texas; Department of Pathology and Immunology, Baylor College of Medicine, Houston, Texas.
10
Department of Molecular and Cell Biology, Alkek Center for Molecular Discovery, Baylor College of Medicine, Houston, Texas; Verna and Marrs McLean Department of Biochemistry, Baylor College of Medicine, Houston, Texas; Advanced Technology Core, Baylor College of Medicine, Houston, Texas.
11
Scott Department of Urology, Baylor College of Medicine, Houston, Texas.
12
Department of Molecular and Cell Biology, Alkek Center for Molecular Discovery, Baylor College of Medicine, Houston, Texas. Electronic address: coarfa@bcm.edu.
13
Department of Molecular and Cell Biology, Alkek Center for Molecular Discovery, Baylor College of Medicine, Houston, Texas; Advanced Technology Core, Baylor College of Medicine, Houston, Texas. Electronic address: putluri@bcm.edu.

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:

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

PMID:
26802582
PMCID:
PMC4861129
DOI:
10.1016/j.juro.2016.01.039
[Indexed for MEDLINE]
Free PMC Article

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