Do you that mean you detected a differentially expressed gene in microarrays (with some method), and then with RNA-seq data from the same samples, you detected it as differentially expressed with one method (edgeR) but not another (DESeq2)?

IMHO that is actually closer to being a validation than not. There are a million reasons why you could see discrepancies between methods, even for the same samples, including, for example:

• the actual sample (RNA) being from the different aliquots, or being processed at different time points
• the 2 techniques being totally different, in the experimental sense and in the data analysis sense
• the statistical methods used by the different softwares being different
• the label of "statistically significant" which we assign to any gene being an arbitrary cutoff
• biological and technical noise which you will always carry when performing a technique
• the differential gene detected having a small change, or your dataset having low power (low number of samples), so that the aforementioned noise will have more impact
• etc.

Because of this, do not expect every gene to validate between 2 different techniques, even on the same samples, even on the same aliquot, etc. (even often doing the same technique two times over!). Moreover, I would think that if the gene (or set of genes) displays a very similar trend of change even if it does not reach statistical significance by a certain method (which we also choose arbitrarily), that could often count as a validation. So IMO the focus should be more in reproducing the trends of change, especially for your sets of genes with the strongest changes, even though sometimes specific genes will not validate for some reason such as the explained above!