Abstract
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.
Highlights
Cells are biological systems that employ a large number of genes and signaling pathways to coordinate multiple functions [1]
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
We found that the PPI perturbations were enriched in some specific genes that were defined as cancer hub genes in our study
Summary
Cells are biological systems that employ a large number of genes and signaling pathways to coordinate multiple functions [1]. Instead of acting in isolation, genes interact with each other and work as part of complex networks [2] The completeness of these networks is the foundation of the normal biological systems, while perturbation of them can result in the pathological state. Distinct mutations will cause different molecular defects in proteins, and they may lead to distinct perturbations of protein networks, giving rise to distinct phenotypic outcomes [4]. In cancer, TP53, a well-known tumor suppressor protein (Fig 1C), loses many interactions with other important proteins, such as PTEN and MDM2 [6]. New proteins, such as CDK4, have been discovered to interact with TP53. In cancer, mutations lead to reconstruction of the protein network rather than the simple destruction, making the protein network in cancer tissues very different from that in normal tissues
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