Integrative Systems Biology Analysis of MicroRNA Regulation in Wnt Signaling Pathway During Breast Cancer Progression

Background: Breast cancer poses a multifaceted challenge in oncology, demanding a comprehensive grasp of its molecular intricacies. Objectives: This in silico systems biology study explores the interplay between microRNA (miRNA) regulation and the Wnt/β-catenin pathway, both pivotal in breast cancer pathogenesis. MicroRNAs (miRNAs) exert influence over fundamental cancer hallmarks, functioning as either oncogenes or tumor suppressors. Methods: In this study, a list of miRNAs known to regulate the Wnt signaling pathway and reported to be dysregulated in breast cancer was retrieved from a recent comprehensive review article. These miRNAs formed the basis for downstream computational analyses. Using a collaborative systems biology framework, we analysed spans dysregulated miRNA expression targets, protein-protein interactions (PPIs), and identification of hub proteins. CytoCluster analysis was performed to identify dense regions in the protein subnetwork. In addition, the exploration of promoter motifs shows the importance of elements in hub proteins. Results: The study uncovers a nuanced miRNA-regulated network in breast cancer, emphasizing 15 pivotal hub proteins among 1 373 targeted proteins. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and gene ontology (GO) enrichment analysis revealed critical pathways and biological processes (BPs) regulated by proteins targeted by these miRNAs. Apart from the regulatory elements of intranuclear transcription, cytoskeleton proteins are also a major part of the targeted proteins in the formation and spread of breast cancer. CytoCluster analysis underscores the direct relevance of dysregulated miRNA-targeted genes to breast cancer. In contrast, promoter motif analysis sheds light on potential regulatory elements influencing hub protein expression, providing an improved comprehension of their roles in the advancement of cancer. Conclusions: This research advances our understanding of breast cancer at a molecular level, offering potential therapeutic targets and insights into signaling cascades, with future validations crucial for translating these findings into actionable interventions and refining breast cancer diagnostics, prognostics, and personalized treatment strategies. Given the in silico nature of this study, limitations related to data source selection and lack of experimental validation may introduce bias. The findings require further biological verification in future studies.