We conducted a comprehensive analysis of a manually curated human signaling network containing 1634 nodes and 5089 signaling regulatory relations by integrating cancer-associated genetically and epigenetically altered genes. We find that cancer mutated genes are enriched in positive signaling regulatory loops, whereas the cancer-associated methylated genes are enriched in negative signaling regulatory loops. We further characterized an overall picture of the cancersignaling architectural and functional organization. From the network, we extracted an oncogenesignaling map, which contains 326 nodes, 892 links and the interconnections of mutated and methylated genes. The map can be decomposed into 12 topological regions or oncogene-signaling blocks, including a few 'oncogene-signaling-dependent blocks' in which frequently used oncogenesignaling events are enriched. One such block, in which the genes are highly mutated and methylated, appears in most tumors and thus plays a central role in cancer signaling. Functional collaborations between two oncogene-signaling-dependent blocks occur in most tumors, although breast and lung tumors exhibit more complex collaborative patterns between multiple blocks than other cancer types. Benchmarking two data sets derived from systematic screening of mutations in tumors further reinforced our findings that, although the mutations are tremendously diverse and complex at the gene level, clear patterns of oncogene-signaling collaborations emerge recurrently at the network level. Finally, the mutated genes in the network could be used to discover novel cancerassociated genes and biomarkers.

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.

Severe Acute Respiratory Syndrome (SARS) is a serious respiratory illness reported in parts of Asia and Canada. A novel coronavirus (CoV) has been isolated and identified as the cause of the SARS for which there is currently no effective treatment. Given the epidemic, the rapid development of efficacious antiviral drugs is needed. The key replicative enzyme SARS CoV main protease (Mpro) represents an attractive target for antiviral chemotherapy. Detailed structural study of the substrate binding cavity led to the generation of a 3-D pharmacophore. Subsets of chemical structures were extracted from the commercial databases by using the defined pharmacophore. Compound mapping to the pharmacophore were docked into the substrate-binding cavity and scored. The selected chemicals were assayed against the SARS CoV Mpro for their inhibitory activity. Three of the compounds showed significant inhibition of the SARS CoV Mpro at low micromolar concentration. This study provides potential lead compounds for specific SARS CoV protease inhibitors. It also signifies the utility of computational techniques for rapid discovery of inhibitors for novel targets.