Mutations or overexpression of signalling genes can result in cancer development and metastasis. In this study, we manually assembled a human cellular signalling network and developed a robust bioinformatics strategy for extracting cancer-associated single nucleotide polymorphisms (SNPs) using expressed sequence tags (ESTs). We then investigated the relationships of cancer-associated genes [cancer-associated SNP genes, known as cancer genes (CG) and cell mobility genes (CMGs)] in a signalling network context. Through a graph-theory-based analysis, we found that CGs are significantly enriched in network hub proteins and cancer-associated genes are significantly enriched or depleted in some particular network motif types. Furthermore, we identified a substantial number of hotspots, the three- and four-node network motifs in which all nodes are either CGs or CMGs. More importantly, we uncovered that CGs are enriched in the convergent target nodes of most network motifs, although CMGs are enriched in the source nodes of most motifs. These results have implications for the foundations of the regulatory mechanisms of cancer development and metastasis.