I'd suggest - first, have a hypothesis. Given what you know about the TCGA biopsies and where they came from, what's your expectation of what a clustering would look like? To get a sense of the data, first make sure the counts are suitably normalized. You could filter the genes to a smaller subset with the highest variance across samples on the log-scale and start clustering with a small set of those genes. Perhaps a few hundred or a thousand, something that is easy to visualize. I would start with using correlation as the similarity measure. Do you you know if the samples came from different clinical sites? Are those sites reflected in the initial clusters? Or the biopsy tissue source? Once you have an initial picture based on a subset of the most variable genes, depending on what you find, you may wish to expand out to include more of the genes, to see what new clusters emerge, if any. Be aware that low-count mRNAs may contribute more noise than signal to your clustering.