Curation-free biomodules mechanisms in prostate cancer predict recurrent disease

James L. Chen, Alexander Hsu, Xinan Yang, Jianrong Li, Younghee Lee, Gurunadh Parinandi, Haiquan Li, Yves A Lussier

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Motivation. Gene expression-based prostate cancer gene signatures of poor prognosis are hampered by lack of gene feature reproducibility and a lack of understandability of their function. Molecular pathway-level mechanisms are intrinsically more stable and more robust than an individual gene. The Functional Analysis of Individual Microarray Expression (FAIME) we developed allows distinctive sample-level pathway measurements with utility for correlation with continuous phenotypes (e.g. survival). Further, we and others have previously demonstrated that pathway-level classifiers can be as accurate as gene-level classifiers using curated genesets that may implicitly comprise ascertainment biases (e.g. KEGG, GO). Here, we hypothesized that transformation of individual prostate cancer patient gene expression to pathway-level mechanisms derived from automated high throughput analyses of genomic datasets may also permit personalized pathway analysis and improve prognosis of recurrent disease. Results: Via FAIME, three independent prostate gene expression arrays with both normal and tumor samples were transformed into two distinct types of molecular pathway mechanisms: (i) the curated Gene Ontology (GO) and (ii) dynamic expression activity networks of cancer (Cancer Modules). FAIME-derived mechanisms for tumorigenesis were then identified and compared. Curated GO and computationally generated «Cancer Module» mechanisms overlap significantly and are enriched for known oncogenic deregulations and highlight potential areas of investigation. We further show in two independent datasets that these pathway-level tumorigenesis mechanisms can identify men who are more likely to develop recurrent prostate cancer (log-rank-p = 0.019). Conclusion: Curation-free biomodules classification derived from congruent gene expression activation breaks from the paradigm of recapitulating the known curated pathway mechanism universe.

Original languageEnglish (US)
Article numberS4
JournalBMC Medical Genomics
Volume6
Issue numberSUPPL2
DOIs
StatePublished - 2013
Externally publishedYes

Fingerprint

Gene Ontology
Prostatic Neoplasms
Microarray Analysis
Gene Expression
Neoplasm Genes
Neoplasms
Carcinogenesis
Genes
Transcriptional Activation
Prostate
Phenotype
Survival
Datasets

ASJC Scopus subject areas

  • Genetics(clinical)
  • Genetics

Cite this

Curation-free biomodules mechanisms in prostate cancer predict recurrent disease. / Chen, James L.; Hsu, Alexander; Yang, Xinan; Li, Jianrong; Lee, Younghee; Parinandi, Gurunadh; Li, Haiquan; Lussier, Yves A.

In: BMC Medical Genomics, Vol. 6, No. SUPPL2, S4, 2013.

Research output: Contribution to journalArticle

Chen, James L. ; Hsu, Alexander ; Yang, Xinan ; Li, Jianrong ; Lee, Younghee ; Parinandi, Gurunadh ; Li, Haiquan ; Lussier, Yves A. / Curation-free biomodules mechanisms in prostate cancer predict recurrent disease. In: BMC Medical Genomics. 2013 ; Vol. 6, No. SUPPL2.
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abstract = "Motivation. Gene expression-based prostate cancer gene signatures of poor prognosis are hampered by lack of gene feature reproducibility and a lack of understandability of their function. Molecular pathway-level mechanisms are intrinsically more stable and more robust than an individual gene. The Functional Analysis of Individual Microarray Expression (FAIME) we developed allows distinctive sample-level pathway measurements with utility for correlation with continuous phenotypes (e.g. survival). Further, we and others have previously demonstrated that pathway-level classifiers can be as accurate as gene-level classifiers using curated genesets that may implicitly comprise ascertainment biases (e.g. KEGG, GO). Here, we hypothesized that transformation of individual prostate cancer patient gene expression to pathway-level mechanisms derived from automated high throughput analyses of genomic datasets may also permit personalized pathway analysis and improve prognosis of recurrent disease. Results: Via FAIME, three independent prostate gene expression arrays with both normal and tumor samples were transformed into two distinct types of molecular pathway mechanisms: (i) the curated Gene Ontology (GO) and (ii) dynamic expression activity networks of cancer (Cancer Modules). FAIME-derived mechanisms for tumorigenesis were then identified and compared. Curated GO and computationally generated «Cancer Module» mechanisms overlap significantly and are enriched for known oncogenic deregulations and highlight potential areas of investigation. We further show in two independent datasets that these pathway-level tumorigenesis mechanisms can identify men who are more likely to develop recurrent prostate cancer (log-rank-p = 0.019). Conclusion: Curation-free biomodules classification derived from congruent gene expression activation breaks from the paradigm of recapitulating the known curated pathway mechanism universe.",
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