Network-based protein-protein interaction prediction method maps perturbations of cancer interactome

The perturbations of protein-protein interactions (PPIs) were found to be the main cause of cancer. Previous PPI prediction methods which were trained with non-disease general PPI data were not compatible to map the PPI network in cancer. Therefore, we established a novel cancer specific PPI prediction method dubbed NECARE, which was based on relational graph convolutional network (R-GCN) with knowledge-based features. It achieved the best performance with a Matthews correlation coefficient (MCC) = 0.84±0.03 and an F1 = 91±2% compared with other methods. With NECARE, we mapped the cancer interactome atlas and revealed that the perturbations of PPIs were enriched on 1362 genes, which were named cancer hub genes. Those genes were found to over-represent with mutations occurring at protein-macromolecules binding interfaces. Furthermore, over 56% of cancer treatment-related genes belonged to hub genes and they were significantly related to the prognosis of 32 types of cancers. Finally, by coimmunoprecipitation, we confirmed that the NECARE prediction method was highly reliable with a 90% accuracy. Overall, we provided the novel network-based cancer protein-protein interaction prediction method and mapped the perturbation of cancer interactome. NECARE is available at: https://github.com/JiajunQiu/NECARE.Author summary: Protein-protein interaction (PPI) network is the biological foundation for the normal function of cells, while the perturbation of this network can result in the pathological state, such as cancer. Notably, the perturbation of PPI network in cancer not only involves in the destruction of old PPI, but also the reconstruction of new PPIs. However, due to the limit of tools, instead of the real physical interaction between proteins, previous cancer network researches only focus on the co-expression relationships. Now, with the development of computational biology, we established a novel cancer specific physical PPI prediction method dubbed NECARE, which was based on relational graph convolutional network (R-GCN) with knowledge-based features. It can infer the PPI in cancer from a general network. And we reveal the cancer PPI interactome by doing high-throughput analysis with NECARE. Also, many cancer hub genes were identified during the analysis, which were enriched for cancer network perturbations. Future studies can benefit from both our method itself and the results of our analysis.