Identification of biomarkers that can accurately and reliably diagnose prostate cancer is clinically highly desirable. A novel classification method, K-closest resemblance was applied to several high-quality transcriptomic datasets of prostate cancer leading to the discovery of a panel of eight gene biomarkers that can detect prostate cancer with over 96% specificity and sensitivity in leave-one-out cross-validation. Independent validation on clinical samples confirmed the discriminatory power of this gene panel, yielding over 95% accuracy of diagnosis based on receiver-operating characteristic curve analyses. Different levels of validation of the proposed biomarker panel have shown that it allows extremely accurate diagnosis of prostate cancer. Application of this panel can possibly add a fast and objective tool to the pathologist's arsenal following further clinical testing.