It is perfectly possible although very unlikely to a gene with only one non-missing value to be top-ranked (when analyzing two color microarray data). It would have to have an extraordinarily large fold change for this to happen.
limma handles missing values in the usual way for linear models at the lmFit() step. A gene with only one value will get df.residual=0. At the shrinkage step, the residual standard deviation for such a gene will be reset to the consensus value across all genes, and the corresponding degrees of freedom will be df.prior. This is explained in the article Smyth, SAGMB, 2004, cited in the documentation.
PS. For a single channel technology, the gene would have to have 2 non-missing values before it could have a fold change and a p-value.