Multiple sclerosis (MS) is a demyelinating disease characterized by an unpredictable clinical course with intermittent relapses that lead over time to significant neurological disability. Clinical and radiological variables are limited in the ability to predict disease course. Peripheral blood genome scale analyses were used to characterize MS patients with different disease types, but not for prediction of outcome. Using complementary-DNA microarrays we studied peripheral-blood gene expression patterns in 53 relapsing-remitting MS patients. Patients were classified into good, intermediate and poor clinical outcome established after 2-year follow-up. A training set of 26 samples was used to identify clinical outcome differentiating gene-expression signature. Supervised learning and feature selection algorithms were applied to identify a predictive signature that was validated in an independent group of 27 patients. Key genes within the predictive signature were confirmed by quantitative reverse transcription-polymerase chain reaction in an additional 10 patients. The analysis identified 431 differentiating genes between patients with good and poor clinical outcome (change in neurological disability by the expanded disability status scale was -0.33 +/- 0.24 and 1.6 +/- 0.35, P = 0.0002, total number of relapses were 0 and 1.80 +/- 0.35, P = 0.00009, respectively). An optimal set of 29 genes was depicted as a clinical outcome predictive gene expression signature and classified appropriately 88.9% of patients. This predictive signature was enriched by genes related biologically to zinc-ion binding and cytokine activity regulation pathways involved in inflammation and apoptosis. Our findings provide a basis for monitoring patients by prediction of disease outcome and can be incorporated into clinical decision-making in relapsing-remitting MS.