AFAIK, PCA is a (truncated) SVD directly on the sample matrix directly. Which amounts to an eigendecomposition of the covariance matrix.

So I don't see any difference between PCA and LSA besides the absence of centering for the later to avoid breaking the sparsity of NLP corpora.