Cancer diagnosis is currently undergoing a paradigm shift with the incorporation of molecular biomarkers as part of routine diagnostic panel. This breakthrough discovery directs researches to examine the role of microRNA in cancer, since its deregulation is often associated with almost all human tumors. Such differences frequently recur in tumor-specific microRNA signatures, which are helpful to diagnose tissue of origin and tumor subtypes. Nonetheless, the resulting classification problem is far from trivial, as there are hundreds of microRNA types, and tumors are non-linearly correlated to the presence of several overexpressions. In this paper, we propose to apply an evolutionary optimized convolutional neural network classifier to this complex task. The presented approach is compared against 21 state-of-the-art classifiers, on a real-world dataset featuring 8129 patients, for 29 different classes of tumors, using 1046 different biomarkers. As a result of the comparison, we also present a meta-analysis on the dataset, identifying the classes on which the collective performance of the considered classifiers is less effective, and thus possibly singling out types of tumors for which biomarker tests might be less reliable.