In Silico Analysis of MiRNA Regulatory Networks to Identify Potential Biomarkers for the Clinical Course of Viral Infections

MiRNA expression profiles exhibit notable alterations in numerous diseases, particularly viral infections. Consequently, miRNAs may be regarded as both therapeutic targets and markers for the development of complications. MiRNAs can significantly influence the modulation of immune responses, offering an extra layer of regulation during viral infections. In this study, miRNAs associated with viral infections were analyzed using an in silico approach. Computer modeling predicted a number of miRNAs capable of influencing the functionality of specific components of the immune system. As a result, 242 miRNAs common to the three types of infections were identified. A network of miRNA-gene regulatory interactions, encompassing 502 nodes (224 miRNAs and 278 genes) and 2236 interactions, was developed. Within this network, subnetworks were identified that are involved in the operation of specific connections in the immune response to viruses. For each step of the immune response, the miRNAs involved in governing these processes were examined. These predicted miRNAs are of particular interest for further analysis aimed at establishing the relationship between their differential expression and disease symptom severity. The obtained data lay the foundation for identifying the most promising molecules as predictive biomarkers and the subsequent development of a diagnostic system.