Link test-A statistical method for finding prostate cancer biomarkers

Xutao Deng, Huimin Geng, Dhundy R. Bastola, Hesham H. Ali

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

We present a new method, link-test, to select prostate cancer biomarkers from SELDI mass spectrometry and microarray data sets. Biomarkers selected by link-test are supported by data sets from both mRNA and protein levels, and therefore results in improved robustness. Link-test determines the level of significance of the association between a microarray marker and a specific mass spectrum marker by constructing background mass spectra distributions estimated by all human protein sequences in the SWISS-PROT database. The data set consist of both microarray and mass spectrometry data from prostate cancer patients and healthy controls. A list of statistically justified prostate cancer biomarkers is reported by link-test. Cross-validation results show high prediction accuracy using the identified biomarker panel. We also employ a text-mining approach with OMIM database to validate the cancer biomarkers. The study with link-test represents one of the first cross-platform studies of cancer biomarkers.

Original languageEnglish (US)
Pages (from-to)425-433
Number of pages9
JournalComputational Biology and Chemistry
Volume30
Issue number6
DOIs
StatePublished - Dec 2006

Keywords

  • Biomarker
  • Mass spectrometry
  • Microarray
  • Prostate cancer
  • Text mining

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Organic Chemistry
  • Computational Mathematics

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