How can give weight to feature before PCA
PCA is unsupervised method for finding the most important components. I don't see a reason why you should want add a weight. If you know what features are important, why use PCA at all? Or perform PCA on the features where you are unsure about the importance.

Further, components are created in directions with highest variance and the importance is measured by eigenvalues. So I can imagine you can somehow increase the variance (like Jan van der Vegt proposed). But that's a sorcery with very questionable output.