A Narrative Review on Integrative Bioinformatics Approaches for microRNA Research in Familial Mediterranean Fever: Current Insights and Future Directions.

Background and Aims: Familial Mediterranean Fever (FMF) is a monogenic autoinflammatory disease caused by mutations in the Methods: A narrative review was conducted by examining published FMF studies that applied miRNA-focused bioinformatics analyses, including miRWalk, TargetScan, and machine learning pipelines. To identify tools with potential relevance to FMF, platforms widely used in rheumatoid arthritis, systemic lupus erythematosus, Crohn's disease, and psoriasis, such as miRDeep2, miRTarBase, DIANA-miRPath, miEAA, and MAGPIE, were evaluated for their analytical strengths and applicability to autoinflammatory pathways. Results: Most FMF studies rely on a narrow set of tools, primarily miRWalk or TargetScan for target prediction. Emerging machine learning approaches have also been utilized to classify patients and explore candidate biomarkers. Other inflammatory diseases use more advanced platforms enabling miRNA discovery, validated interaction mapping, pathway enrichment, and multi-omics integration. Tools such as miRDeep2, miRTarBase, DIANA-miRPath, miEAA, and MAGPIE remain underutilized in FMF. Key limitations include small cohorts, patient heterogeneity, and limited experimental validation. Conclusion: Broadening the bioinformatics toolkit for FMF miRNA research could significantly enhance biomarker identification and mechanistic insight. Larger datasets, integrated analysis pipelines, and cross-disciplinary collaboration are essential to advancing precision diagnostics and targeted therapies for FMF.