From NPC therapeutic target identification to potential treatment strategy

Nasopharyngeal carcinoma (NPC) is relatively rare in Western countries but is a common cancer in southern Asia. Many differentially expressed genes have been linked to NPC; however, how to prioritize therapeutic targets and potential drugs from unsorted gene lists remains largely unknown. We first collected 558 upregulated and 993 downregulated NPC genes from published microarray data and the primary literatures. We then postulated that conversion of gene signatures into the protein-protein interaction network and analyzing the network topologically could provide insight into key regulators involved in tumorigenesis of NPC. Of particular interest was the presence of cliques, called fully connected subgraphs, in the inferred NPC networks. These clique-based hubs, connecting with more than three queries and ranked higher than other nodes in the NPC protein-protein interaction network, were further narrowed down by pathway analysis to retrieve 24 upregulated and 6 downregulated bottleneck genes for predicting NPC carcinogenesis. Moreover, additional oncogenes, tumor suppressor genes, genes involved in protein complexes, and genes obtained after functional profiling were merged with the bottleneck genes to form the final gene signature of 38 upregulated and 10 downregulated genes. We used the initial and final NPC gene signatures to query the Connectivity Map, respectively, and found that target reduction through our pipeline could efficiently uncover potential drugs with cytotoxicity to NPC cancer cells. An integrative Web site (http://140.109.23.188:8080/NPC) was established to facilitate future NPC research. This in silico approach, from target prioritization to potential drugs identification, might be an effective method for various cancer researches.