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

Research output: Contribution to journalArticle

24 Citations (Scopus)

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.

Original languageEnglish (US)
JournalJournal of Urology
DOIs
StateAccepted/In press - 2016

Fingerprint

Atlases
Metabolic Networks and Pathways
Urinary Bladder Neoplasms
Genome
Survival
Genes
Mass Spectrometry
Neoplasms
Transcriptome
Chemical Databases
Databases
Encyclopedias
Gene Ontology
Dermatoglyphics
Liquid Chromatography
Urinary Bladder

Keywords

  • Mass spectrometry
  • Metabolic networks and pathways
  • Metabolomics
  • Urinary bladder neoplasms
  • Urothelium

ASJC Scopus subject areas

  • Urology

Cite this

Integrative Pathway Analysis of Metabolic Signature in Bladder Cancer : A Linkage to The Cancer Genome Atlas Project and Prediction of Survival. / von Rundstedt, Friedrich Carl; Rajapakshe, Kimal; Ma, Jing; Arnold, James M.; Gohlke, Jie; Putluri, Vasanta; Krishnapuram, Rashmi; Piyarathna, D. Badrajee; Lotan, Yair; Gödde, Daniel; Roth, Stephan; Störkel, Stephan; Levitt, Jonathan M.; Michailidis, George; Sreekumar, Arun; Lerner, Seth P.; Coarfa, Cristian; Putluri, Nagireddy.

In: Journal of Urology, 2016.

Research output: Contribution to journalArticle

von Rundstedt, FC, Rajapakshe, K, Ma, J, Arnold, JM, Gohlke, J, Putluri, V, Krishnapuram, R, Piyarathna, DB, Lotan, Y, Gödde, D, Roth, S, Störkel, S, Levitt, JM, Michailidis, G, Sreekumar, A, Lerner, SP, Coarfa, C & Putluri, N 2016, 'Integrative Pathway Analysis of Metabolic Signature in Bladder Cancer: A Linkage to The Cancer Genome Atlas Project and Prediction of Survival', Journal of Urology. https://doi.org/10.1016/j.juro.2016.01.039
von Rundstedt, Friedrich Carl ; Rajapakshe, Kimal ; Ma, Jing ; Arnold, James M. ; Gohlke, Jie ; Putluri, Vasanta ; Krishnapuram, Rashmi ; Piyarathna, D. Badrajee ; Lotan, Yair ; Gödde, Daniel ; Roth, Stephan ; Störkel, Stephan ; Levitt, Jonathan M. ; Michailidis, George ; Sreekumar, Arun ; Lerner, Seth P. ; Coarfa, Cristian ; Putluri, Nagireddy. / Integrative Pathway Analysis of Metabolic Signature in Bladder Cancer : A Linkage to The Cancer Genome Atlas Project and Prediction of Survival. In: Journal of Urology. 2016.
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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 = "Mass spectrometry, Metabolic networks and pathways, Metabolomics, Urinary bladder neoplasms, Urothelium",
author = "{von Rundstedt}, {Friedrich Carl} and Kimal Rajapakshe and Jing Ma and Arnold, {James M.} and Jie Gohlke and Vasanta Putluri and Rashmi Krishnapuram and Piyarathna, {D. Badrajee} and Yair Lotan and Daniel G{\"o}dde and Stephan Roth and Stephan St{\"o}rkel and Levitt, {Jonathan M.} and George Michailidis and Arun Sreekumar and Lerner, {Seth P.} and Cristian Coarfa and Nagireddy Putluri",
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T2 - A Linkage to The Cancer Genome Atlas Project and Prediction of Survival

AU - von Rundstedt, Friedrich Carl

AU - Rajapakshe, Kimal

AU - Ma, Jing

AU - Arnold, James M.

AU - Gohlke, Jie

AU - Putluri, Vasanta

AU - Krishnapuram, Rashmi

AU - Piyarathna, D. Badrajee

AU - Lotan, Yair

AU - Gödde, Daniel

AU - Roth, Stephan

AU - Störkel, Stephan

AU - Levitt, Jonathan M.

AU - Michailidis, George

AU - Sreekumar, Arun

AU - Lerner, Seth P.

AU - Coarfa, Cristian

AU - Putluri, Nagireddy

PY - 2016

Y1 - 2016

N2 - 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.

AB - 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.

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KW - Metabolomics

KW - Urinary bladder neoplasms

KW - Urothelium

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