Characterizing SARS-CoV-2 oncogenic features from protein-protein multilayer interaction networks

In recent years, network-based approaches have become increasingly popular in the field of molecular biology and network medicine. One of the main challenges in this field is to identify the role of viruses in the development of cancer. In this thesis, we apply multilayer complex network theory to protein-protein interaction networks of different viruses, including the SARS-CoV-2 virus, to classify them as oncogenic or non-oncogenic. Specifically, we use tools from graph theory and machine learning to analyze the topology and structure of these networks, and to identify the key proteins and pathways involved in virus-induced carcinogenesis. We aim to create two classes, one for oncogenic and one for non-oncogenic viruses, and then verify in which of the two the SARS-CoV-2 virus falls. The results of this study may provide new insights into the mechanisms underlying virus-induced cancer and could lead to the development of novel therapeutic strategies.