Applying random forests to identify biomarker panels in serum 2D-DIGE data for the detection and staging of prostate cancer.

In recent years, Prostate Specific Antigen (PSA) testing is widespread and has been associated with deceased mortality rates; however, this testing has raised concerns of overdiagnosis and overtreatment. It is clear that additional biomarkers are required. To identify these biomarkers, we have undertaken proteomics and metabolomics expression profiles of serum samples from BPH, Gleason score 5 and 7 using two-dimensional difference in gel electrophoresis (2D-DIGE) and nuclear magnetic resonance spectroscopy (NMR). Panels of serum protein biomarkers were identified by applying Random Forests to the 2D-DIGE data. The evaluation of selected biomarker panels has shown that they can provide higher prediction accuracy than the current diagnostic standard. With careful validation of these serum biomarker panels, these panels may potentially help to reduce unnecessary invasive diagnostic procedures and more accurately direct the urologist to curative surgery.