Biomarker Selection System, Employing an Iterative Peak Selection Method, for Identifying Biomarkers Related to Prostate Cancer

A biomarker selection system is proposed for identifying biomarkers related to prostate cancer. MS-spectra were obtained from the National Cancer Institute Clinical Proteomics Database. The system comprised two stages, a preprocessing stage, which is a sequence of MS-processing steps consisting of MSspectrum smoothing, novel iterative peak selection, peak alignment, and a classification stage employing the PNN classifier. The proposed iterative peak selection method was based on first applying local thresholding, for determining the MS-spectrum noise level, and second applying an iterative global threshold estimation algorithm, for selecting peaks at different intensity ranges. At each global threshold, an optimum sub-set of these peaks was used to design the PNN classifier for highest performance, in discriminating normal cases from cases with prostate cancer, and thus indicate the best m/z values. Among these values, the information rich biomarkers 1160.8, 2082.2, 3595.9, 4275.3, 5817.3, 7653.2, that have been associated with the prostate gland, are proposed for further investigation.