Unraveling the network signatures of oncogenicity in virus-human protein-protein interactions

Abstract

Climate change, increasing urbanization and global human mobility have increased the risk of emergent infectious diseases and the risk for their pandemic potential. Consequently, there is a pressing need for methods that can provide rapid insights about the potential enduring effects on the human hosts. In-silico methods can be more suitable than in-vitro and in-vivo methods to identify effects potentially manifesting after a long time, under the hypothesis that the underlying causes can be ascribed to perturbed biomolecular processes. Here we focus on oncogenicity, i.e., the risk of developing cancer subsequent to a viral infection, a phenomenon estimated to account for approximately 15% of global cancer prevalence. We characterize viruses in terms of their oncogenic potential by analyzing a multilayer representation of protein-protein interaction networks reconstructed from the human interactome. Our frame-work facilitates classification and allows us to identify sub-sets of proteins in-volved in oncogenesis, highlighting the mechanisms underpinning viral oncogenicity, including factors like chromatin structure. Our framework serves as a foundational resource for medical research, paving the way for the study of other potential long-term effects associated with emerging viruses.