A large scale analysis of gene expression data, performed by Segal and colleagues, identified sets of genes named Cancer Modules (CMs), involved in the onset and progression of cancer. By using functional interaction network data derived from different sources of biomolecular information, we show that random walks and label propagation algorithms are able to correctly rank genes with respect to CMs. In particular, the random walk with restart algorithm (RWR), by exploiting both the global topology of the functional interaction network, and local functional connections between genes relatively close to CM genes, achieves significantly better results than the other compared methods, suggesting that RWR could be applied to discover novel genes involved in the biological processes underlying tumoral diseases.