For RNA-seq data you can use DESeq2 or EdgeR to perform a differential expression analysis. These tools are part of Bioconductor in R. However, both of these programs perform their own internal normalizations and they recommend you input the raw counts, not the RPKM or RSEM scaled estimates (for RNAseqV2 TCGA data). If you are downloading from the TCGA Portal each patient's data is in a separate file, but if you go to https://confluence.broadinstitute.org/display/GDAC/Dashboard-Stddata and click on the Open link beside the cancer of interest you will find tar files that contain merged text files with all patients in one file. Once you get all the raw counts or normalized counts in a single matrix you can analyze the data with any program that accepts a matrix of data; BUT make sure it is meant to be used on RNA-seq data because this type of data has different properties than microarray data and needs to be treated slightly differently when genes have low or zero read counts. In doing my own comparisons between DESeq2 and EdgeR, I have found I prefer DESeq2 results because it compensates for low reads counts, which can artificially inflate fold changes.