A Network Analysis Approach to Detect and Differentiate Usher Syndrome Types Using miRNA Expression Profiles: A Pilot Study

Background: Usher syndrome (USH) is a rare genetic disorder that affects both hearing and vision. It presents in three clinical types—USH1, USH2, and USH3—with varying onset, severity, and disease progression. Existing diagnostics primarily rely on genetic profiling to identify variants in USH genes; however, accurate detection before symptom onset remains a challenge. MicroRNAs (miRNAs), which regulate gene expression, have been identified as potential biomarkers for disease. The aim of this study is to develop a data-driven system for the identification of USH using miRNA expression profiles. Methods: We collected microarray miRNA-expression data from 17 samples, representing four patient-derived USH cell lines and a non-USH control. Supervised feature selection was utilized to identify key miRNAs that differentiate USH cell lines from the non-USH control. Subsequently, a network model was constructed by measuring pairwise correlations based on these identified features. Results: The proposed system effectively distinguished between control and USH samples, demonstrating high accuracy. Additionally, the model could differentiate between the three USH types, reflecting its potential and sensitivity beyond the primary identification of affected subjects. Conclusions: This approach can be used to detect USH and differentiate between USH subtypes, suggesting its potential as a future base model for the identification of Usher syndrome.