Identification of Disease-miRNA Networks Across Different Human Complex Diseases Using SWIM and MIENTURNET Tools

miRNAs are small endogenous noncoding RNAs widely known for their pivotal role as key post-transcriptional gene regulators. They exert their effects through binding to specific regions of transcripts with imperfect sequence complementarity. miRNAs are deeply involved in cell growth, differentiation, development, and apoptosis and have a critical role in numerous diseases. In this context, the prediction of miRNA targets stands as a crucial step toward the development of new treatments and innovative therapeutic approaches. However, the prediction of miRNA-binding sites and the prioritization of miRNA-target interactions are often based exclusively on sequence complementarity and present notable challenges. In this context, network-based approaches have emerged as powerful tools to capture complex relationships among miRNAs and their targets and to provide relevant insights into their regulation. In this chapter, we present the application of SWIM to a wide range of complex diseases to identify a group of "switch miRNAs" characterized by distinctive topological properties and a major involvement in key phenotypic transitions. Furthermore, we highlight the use of MIENTURNET, an innovative web-based tool designed to perform statistical and network-based analyses, enabling the identification and prioritization of miRNA-target interactions.