Link test-A statistical method for finding prostate cancer biomarkers

Authors:
Xutao Deng;Huimin Geng;Dhundy R. Bastola;Hesham H. Ali
Affiliations:
College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA;Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE 68198, USA;Department of Pediatrics, University of Nebraska Medical Center, Omaha, NE 68198, USA;College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA
Venue:
Computational Biology and Chemistry
Year:
2006

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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.