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Mass spectrometry (MS) data has been widely analyzed for the detection of early stage cancers. Its potential for seeking proteomic biomarkers has received a great deal of attention in recent years. In the sparse representation classification (SRC) framework, a testing sample is represented as a sparse linear combination of training samples. The coefficient vector of representation is obtained by a ℓ1-norm regularized least square method. Classification results are achieved by defining discriminant functions from the coefficient vector for each category. In this paper, a novel feature selection method based on SRC was proposed. To investigate its performance, the proposed methods was tested and evaluated on the ovarian cancer database OC-WCX2a and OC-WCX2b. The experimental results showed that SRC is efficient for tumor classification. Feature selection based on sparse representation (SRFS) can select highly predictive representative feature sets.