| Popis: |
The functional knowledge of cancer proteins and cancer pathways is currently limited and not detailed enough, remaining as a major hurdle to cancer studies. Particularly, many cancer proteins are only annotated to high-level general GO categories. Here, we apply an efficient algorithm, by constructing function-specific protein-protein interaction sub-networks, to find finer functions of the cancer proteins compiled in Cancer Gene Census database. By exploiting their previously known functions, 193 cancer proteins are predicted to finer functional categories, with F score higher than 0.6. Furthermore, because cancer proteins contribute to carcinogenesis through alterations of some essential cellular functions, discovering additional proteins involved in such functions is also of importance for uncovering the mechanisms of cancer. To approximate cancer functions, 37 specific functions significantly enriched with known cancer proteins are selected. With F score higher than 0.6, 221 proteins are predicted to these cancer functions, improving the connection of the function-specific interaction sub-networks and thus delineating cancer functions more integrally and clearly. |
