PCA and similar techniques are quite useful for exploratory data analysis in bioinformatics, in particular determining which experimental factors or batch effects have the largest influence on the data. Generally, if you can't identify your effect of interest in the first few principal components of a gene expression dataset, you're going to find few, if any, significant genes.