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In recent years, there has been a growing interest in developing innovative anticancer therapies targeting the tumor microenvironment (TME). The TME is a complex and dynamic milieu surrounding the tumor mass, consisting of various cellular and molecular components, including those from the host organism, endowed with the ability to significantly influence cancer development and progression. Processes such as angiogenesis, immune evasion, and metastasis are crucial targets in the search for novel anticancer drugs. Thus, identifying molecules with “multi-tasking” properties that can counteract cancer cell growth at multiple levels represents a relevant but still unmet clinical need. Extensive research over the past two decades has revealed a consistent anticancer activity for several members of the T2 ribonuclease family, found in evolutionarily distant species. Initially, it was believed that T2 ribonucleases mainly acted as anticancer agents in a cell-autonomous manner. However, further investigation uncovered a complex and independent mechanism of action that operates at a non-cell-autonomous level, affecting crucial processes in TME-induced tumor growth, such as angiogenesis, evasion of immune surveillance, and immune cell polarization. Here, we review and discuss the remarkable properties of ribonucleases from the T2 family in the context of “multilevel” oncosuppression acting on the TME.

Given the key role of innate immunity in both defense against pathogens and tissue regeneration, innovative studies are becoming crucial to provide further information on how both processes are linked together and to clarify how immune cells perform the coordinated regulation of the aforementioned processes. The present review is mainly focused on two proteins that have been recently found to carry out critical functions in innate immune system regulation, i. e. the Allograft inflammatory factor-1 (AIF-1) and RNASET2, a protein belonging to the T2 ribonuclease family. Their crucial role in both the activation and modulation of the inflammatory response and in the remodeling of connective tissue during grafts and wound repair have been thoroughly investigated in the medicinal leech and will pave the way for novel therapeutic approaches to control immune and systemic responses to disease, injury, and bacterial infection, based on the functionalities of these biomolecules.

Meta-analysis of high-throughput gene expression data is often used for the interpretation of proprietary gene expression data sets. We have recently shown that co-occurrence patterns of gene expression in published cancer-related gene expression signatures are reminiscent of several cancer signaling pathways. Indeed, significant co-occurrence of up to ten genes in published gene expression signatures can be exploited to build a co-occurrence network from the sets of co-occurring genes (“co-occurrence modules”). Such co-occurrence network is represented by an undirected graph, where single genes are assigned to vertices and edges indicate that two genes are significantly co-occurring. Thus, graph-cut methods can be used to identify groups of highly interconnected vertices (“network communities”) that correspond to sets of genes that are significantly co-regulated in human cancer. Here, we investigate the topological properties of co-occurrence networks derived from published gene expression signatures and show that co-occurrence networks are characterized by scale-free topology and hierarchical modularity. Furthermore, we report that genes with a “promiscuous” or a “faithful” co-occurrence pattern can be distinguished. This behavior is reminiscent of date and party hubs that have been identified in protein-protein interaction network

Meta-analysis of high-throughput gene expression data is often used for the interpretation of proprietary gene expression data sets. We have recently shown that co-occurrence patterns of gene expression in published cancer-related gene expression signatures are reminiscent of several cancer signaling pathways. Indeed, significant co-occurrence of up to ten genes in published gene expression signatures can be exploited to build a co-occurrence network from the sets of co-occurring genes (“co-occurrence modules”). Such co-occurrence network is represented by an undirected graph, where single genes are assigned to vertices and edges indicate that two genes are significantly co-occurring. Thus, graph-cut methods can be used to identify groups of highly interconnected vertices (“network communities”) that correspond to sets of genes that are significantly co-regulated in human cancer. Here, we investigate the topological properties of co-occurrence networks derived from published gene expression signatures and show that co-occurrence networks are characterized by scale-free topology and hierarchical modularity. Furthermore, we report that genes with a “promiscuous” or a “faithful” co-occurrence pattern can be distinguished. This behavior is reminiscent of date and party hubs that have been identified in protein-protein interaction networks.