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

The inclusion of bioinformatics into FMF research holds significant potential. By combining computational analyses with molecular and clinical data, researchers can map regulatory networks driving inflammation and disease variability in patients. Advanced data‐driven approaches could help identify potential gene targets and signaling pathways influenced by miRNAs, improving understanding of how dysregulated posttranscriptional control contributes to disease progression. Predictive modeling and multi‐omics analyses could enable anticipation of flares, assessment of treatment response, and detection of subtle molecular patterns that conventional methods may miss. Although exploratory, these approaches could transition FMF research toward a more precise, mechanistic, and individualized field. For greater accuracy, multiple bioinformatics tools are often used sequentially or in combination. For instance, in RA, initial miRNA target predictions using TargetScan are cross‐referenced with miRDB, and high‐confidence interactions are validated through miRTarBase and reporter assays [ 38 ]. Likewise, DIANA‐miRPath and IPA are applied for pathway enrichment: DIANA assesses regulatory impacts via KEGG/GO databases, while IPA links affected genes to upstream regulators and canonical pathways. This strategy showed that miRNA expression changes in T‐cell signaling and cytokine cascades in SLE [ 57 ]. Recent advancements also involve integrating ML models with transcriptomic data to predict FMF‐related outcomes such as flare frequency and drug responsiveness, leveraging miRNA targets from miRTarBase and pathway annotations from IPA [ 78 , 90 ]. Together, these tools provide a synergistic framework: prediction algorithms refine target selection, databases validate interactions, and functional analysis tools elucidate biological significance. Despite these advancements, bioinformatics faces key limitations that hinder its use in FMF research. Large, inclusive FMF‐specific datasets are scarce, as most studies rely on small cohorts [ 97 ]. Variations in patient populations and laboratory processes reduce reproducibility and generalizability [ 10 ]. Additionally, computational predictions, such as miRNA targets or pathogenic variants, require experimental validation [ 98 ]. Clinical heterogeneity adds another layer of complexity [ 30 ], necessitating approaches that can accommodate diverse phenotypes and confounders. Moreover, the overlap of inflammatory pathways with other diseases makes it challenging to distinguish FMF‐specific signals from generalized inflammatory noise [ 99 ]. Addressing these issues requires careful study design with adequate sample sizes, matched controls, cross‐validation, and integration of computational, experimental, and clinical data. Future FMF research should focus on multi‐center data collection, similar to IBD and SLE initiatives [ 100 , 101 ]. Validation of computational predictions via experimental methods, such as luciferase reporter assays or biomarker testing in patient samples, is essential to translate findings into clinical insights. Integrating multi‐omics data can uncover comprehensive molecular signatures in FMF. ML algorithms applied to longitudinal patient data can further support predictive modeling of disease flares and treatment responses, as demonstrated in SLE and RA.

Zeinab Skaineh: investigation, writing – original draft preparation (equal). Razane Hammoud: investigation, writing – original draft preparation (equal). Ahlam Chaaban: conceptualization, review and editing. Eliana Eldawra: supervision, review and editing. José‐Noel Ibrahim: conceptualization, supervision, review and editing.

The authors received no specific funding for this work.

A comprehensive literature search strategy was employed across various databases, such as PubMed and Google Scholar, using combinations of the following keywords: “Familial Mediterranean Fever” or “FMF” and “microRNA” or “miRNA” and “bioinformatics” or “bioinformatics tools” or “miRNA profiling” or “target prediction” or the names of specific tools. Only peer‐reviewed studies published in English were included. Priority was given to research focusing on FMF‐specific miRNA profiles, studies utilizing tools directly applied to FMF datasets, and analyses of MEFV gene regulation mediated by miRNAs. Given the limited number of FMF‐specific miRNA studies, additional literature from related autoinflammatory or autoimmune conditions was incorporated, along with general miRNA bioinformatics research, when relevant methodologies could be adapted to FMF. The included publications comprised original research articles, methodological tool papers, and official software documentation, providing a comprehensive overview of applicable approaches and resources. The screening process was carried out in two stages: initially, by reviewing titles and abstracts to assess topic relevance, and subsequently through full‐text evaluation to confirm eligibility for inclusion. The selected studies were then assessed based on the bioinformatics methods used, their functional scope, and their relevance and applicability to FMF research.

Familial Mediterranean Fever (FMF) is a monogenic autoinflammatory disease most common in populations around the Mediterranean Sea. The disease usually begins in childhood or adolescence and is marked by recurrent episodes of fever, serositis, arthritis, and systemic inflammation [ 1 , 2 ]. Daily colchicine remains the primary therapy for FMF, effectively reducing the frequency and severity of attacks as well as the risk of amyloidosis, the most severe complication of the disease [ 3 , 4 ]. For patients who are intolerant or resistant to colchicine, interleukin‐1 (IL‐1) inhibitors offer an effective alternative [ 5 ]. FMF is caused by mutations in the MEFV (MEditerranean FeVer) gene, which is located on chromosome 16p13.3 [ 6 ]. This gene comprises 10 exons and encodes pyrin, a cytosolic protein composed of 781 amino acids. Pyrin acts as a key regulator of the inflammasome, a multiprotein complex involved in the activation of caspase‐1 and the production of pro‐inflammatory cytokines, mainly IL‐1β, and IL‐18 [ 7 , 8 ]. FMF mutations impair the phosphorylation‐dependent regulation of pyrin, resulting in inappropriate inflammasome assembly, caspase‐1 activation, and excessive secretion of IL‐1β and IL‐18. This leads to ongoing subclinical inflammation, which drives the recurrent inflammatory episodes characteristic of FMF [ 1 , 3 ]. More than 400 MEFV variants have been identified to date, with M694V, M694I, M680I, and V726A among the most pathogenic and prevalent alleles [ 9 , 10 ]. The diagnosis of FMF is primarily clinical, based on characteristic features of the disease. It is often supported by a relevant ethnic background, a positive family history, and a favorable response to colchicine therapy [ 11 ]. Since the identification of the MEFV gene in the late 1990s, molecular testing has become central to FMF diagnosis and has helped reduce delays in treatment [ 12 , 13 ]. Additionally, laboratory markers of systemic inflammation, such as leukocyte count, erythrocyte sedimentation rate, C‐reactive protein, serum amyloid A, and fibrinogen levels, are often assessed, particularly during acute attacks, to support the clinical diagnosis [ 14 ]. However, some patients who fulfill clinical criteria, despite having only one detectable MEFV mutation, may still develop FMF symptoms, underscoring the complexity of genotype–phenotype correlations and the limitations of genetic testing alone [ 15 ]. Moreover, the wide variability in clinical presentation, attributed to MEFV allelic heterogeneity, modifier genes, and epigenetic modifications, challenges the development of standardized diagnostic approaches for FMF and may impact disease severity and treatment response [ 16 , 17 , 18 ]. Considering these limitations, there is growing interest in identifying novel molecular markers that can improve FMF diagnosis. One such promising class of biomarkers is microRNAs (miRNAs). miRNAs are small, noncoding RNA molecules, typically 16–24 nucleotides in length [ 19 ]. Through sequence‐specific interactions with messenger RNAs (mRNAs), miRNAs regulate gene expression posttranscriptionally, thereby modulating key inflammatory and immune pathways implicated in FMF [ 20 ]. Recent studies have demonstrated that miRNA expression profiles are altered in FMF patients during both attacks and remission. For instance, miR‐146a, miR‐155, and miR‐4520a have been reported to modulate inflammasome and innate immune pathways, and to directly interact with MEFV transcripts [ 21 , 22 ]. Their dysregulation implies that specific miRNAs could serve as noninvasive biomarkers for diagnosis, disease monitoring, or treatment response [ 19 , 23 ]. miRNA data analysis presents notable challenges. The expression profiles are complex, highly context‐dependent, and often involve large‐scale datasets generated by high‐throughput platforms [ 24 , 25 ]. To accurately interpret this information, the application of bioinformatics tools is crucial. These tools enable the detection and profiling of miRNA in biological samples, prediction of miRNA–mRNA interactions, and their mapping to relevant biological pathways and functions [ 26 ]. Originally developed for oncology and systemic immune diseases, some of these platforms are now being adapted for FMF research, enhancing understanding of its molecular mechanisms and aiding in identifying novel diagnostic markers and therapeutic targets [ 27 ]. Nevertheless, their application to FMF‐miRNA datasets remains limited, constraining FMF‐specific conclusions. This review highlights an important gap in FMF research: the limited use of bioinformatics tools for miRNA analysis. To address this, we examined tools currently employed in FMF‐miRNA studies, such as miRbase, TargetScan, miRWalk, and custom machine learning (ML) pipelines, and identified additional computational resources successfully used in other diseases but underutilized in FMF. Broader adoption of these tools could advance miRNA‐based analyses and facilitate the discovery of FMF‐specific biomarkers.

The study of miRNAs in FMF is increasingly becoming a key focus in FMF research. While several bioinformatics tools have contributed to identifying dysregulated miRNAs and potential targets, their integration in FMF remains underdeveloped compared to other inflammatory diseases. Tools like miRDeep2, miRDB, miRTarBase, DIANA‐miRPath, IPA, and MAGPIE, though not yet widely applied in FMF, have successfully mapped immune pathways and gene regulation in related conditions and could be adapted for FMF. Strategically combining these tools offers a powerful pipeline for unraveling disease‐specific mechanisms. Key challenges include the limited availability of large, high‐quality FMF datasets, the complexity of genotype–phenotype correlations, and the need for experimental validation of in silico predictions. Overcoming these limitations will require interdisciplinary data‐sharing platforms, integrated multi‐omics analyses, and the application of ML. With these advancements, bioinformatics can shift from a supportive role to a central tool in FMF research, facilitating the identification of novel diagnostic markers and enabling more effective, personalized treatment strategies. The future of FMF precision medicine will depend on applying these computational resources to clarify the molecular complexity of the disease.

The lead author José‐Noel Ibrahim affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.

The authors declare no conflicts of interest.

miRDeep2 is a computational tool utilized to accurately detect and quantify both known and novel miRNAs from deep sequencing data. It is valuable for documenting changes in miRNA expression across various conditions. In SLE research, miRDeep2 has helped identify miRNAs that are differentially expressed between diseased and healthy tissues [ 43 ]. Similarly, using miRDeep2 for small RNA analysis in patients with SLE and psoriasis has revealed novel miRNAs associated with these diseases [ 44 ]. This wide adoption allowed miRDeep2 to gain popularity and become widely recognized [ 46 ]. Nevertheless, miRDeep2 has technical limitations, such as over‐counting sequence reads and difficulty detecting variants with insertions or deletions (indels) [ 46 ]. miRDB is an online resource for predicting miRNA targets and elucidating their functions [ 47 ]. Its strength lies in the use of MirTarget, an ML algorithm that uses a support vector machine trained on validated sequencing results to predict targets and assign functional relevance [ 48 , 50 ]. miRDB has been applied in RA and SLE to identify miRNA‐regulated genes involved in inflammation, uncovering candidates with potential as therapeutic targets or disease biomarkers [ 48 , 49 ]. A limitation of miRDB is the large number of predicted targets, leading to false positives, hence necessitating data filtering and validation [ 51 ]. On the other hand, miRTarBase provides experimentally confirmed miRNA–target interactions (MTIs) using methods such as qPCR and reporter assays [ 52 ]. With over 3.8 million validated MTIs, it is one of the most comprehensive resources for confirmed miRNA targets [ 93 ]. In SLE, miRTarBase was employed to construct miRNA–mRNA networks and integrate experimental validation to ensure reliability [ 94 , 95 ]. It is also frequently used in RA to validate MTIs involved in cytokine production and immune cell activation [ 53 , 54 ]. Overall, miRTarBase offers a reliable, experimentally validated database that enhances the credibility of bioinformatics analyses [ 55 ]. Its main limitation is incomplete coverage, as many known interactions are omitted [ 24 ]. DIANA‐miRPath is an online platform that compiles multiple databases and miRNA target prediction algorithms, such as microT‐CDS and TarBase/miRTarBase, along with Kyoto encyclopedia of genes and genomes (KEGG) and gene ontology (GO) data, to identify pathways affected by a set of miRNAs [ 57 ]. It has been widely used in immune‐mediated disease research to interpret the functional impact of miRNA changes. In SLE, DIANA‐miRPath revealed that immune and inflammatory pathways in KEGG were enriched by disease‐associated miRNAs [ 57 ]. In RA, it identified pathways such as cytokine signaling and T cell receptor pathways affected by targets of differentially expressed miRNAs. Similarly, in psoriasis, the tool has been employed to explore miRNA targets and their association with key cellular processes such as adhesion, migration, and proliferation, hinting at their relevance in psoriasis development and progression [ 58 ]. DIANA‐miRPath performs pathway enrichment analyses for miRNAs across multiple species using integrated data and robust statistical methods [ 61 ]. However, enrichment results may be biased when based solely on predicted targets, as random miRNA sets can yield false‐positive associations [ 62 ]. Ingenuity pathway analysis (IPA) is a commercial tool that contextualizes gene expression, proteomics, or miRNA data within critical biological networks and pathways [ 63 ]. By analyzing gene lists or expression profiles, IPA identifies enriched canonical pathways, network associations between genes, and upstream regulators. This tool is frequently used in autoimmune disease research, particularly transcriptomic and proteomic studies. In RA, IPA revealed pathways linked to inflammation and pain by uncovering networks of genes associated with immune responses and cytokine regulation [ 64 ]. In UC and psoriasis, it facilitated the discovery of key pathways involving interferon and TNF, as well as master regulators such as transcription factors and cytokines, linking molecular information to known disease mechanisms [ 65 ]. In SLE, IPA clarified gene expression profiles and highlighted key regulatory pathways [ 66 ]. IPA's strengths include a curated knowledge base that allows detailed examination of relationships and causal links within pathways [ 67 ]. However, it focuses on humans and well‐characterized model species, potentially missing novel or context‐specific interactions [ 68 ]. Furthermore, as a proprietary tool, IPA can be less accessible and less transparent compared to public databases [ 68 ]. miRNA Enrichment Analysis and Annotation (miEAA) facilitates the study of miRNA functions by conducting over‐representation analysis (ORA) or gene set enrichment analysis (GSEA) for various functional groups [ 69 ]. Tailored for miRNA data, it includes extensive reference sets connecting miRNAs to biological processes. For example, the use of miEAA in autoimmune diseases highlighted dysregulated miRNAs linked to T‐cell activation and cytokine signaling in osteoarthritis [ 70 ]. The key strengths of miEAA are its ability to perform ORA and GSEA tailored for miRNAs and its extensive annotation database, enhancing functional interpretation [ 62 ]. However, its limitations include restricted applicability to well‐studied species [ 96 ]. Although updating to newer versions has expanded species options, some rare animals are still not included. Multimodal Annotation Generated Pathogenic Impact Evaluator (MAGPIE) is an ML‐based tool that combines several annotations, such as sequences, conservation, and functional data, to predict the pathogenicity of genetic variants [ 72 ]. Introduced in 2024, MAGPIE was trained and validated on numerous pathogenic and benign variants from ClinVar, demonstrating accuracy in interpreting Mendelian disorders by analyzing epigenetic, structural, and evolutionary features of variants [ 72 ]. MAGPIE showcased superior predictive power, effectively interpreting rare variants, and can be applied to many types of genomic variant analyses [ 72 ]. However, since MAGPIE studies each variant individually, it is less effective at recognizing complex or polygenic diseases [ 72 ]. It also fails to integrate patient phenotypes or medical records, which may reduce accuracy when clinical context is needed [ 72 ]. Furthermore, MAGPIE performs better on exonic regions than on noncoding areas, and reliance on multiple annotation tools results in slower runtimes, potentially limiting its clinical application [ 72 ]. Cytoscape is a free, open‐source tool for visualizing, drawing, and exploring biological networks, where nodes represent molecules, such as proteins or genes, and lines represent their interactions. The platform enables network arrangement, search for specific elements, overlay of gene‐expression or phenotype data, and integration with functional annotation databases [ 85 ]. A major advantage is its plug‐in system, which supports over 250 add‐ons for tasks such as module detection, pathway enrichment, and external databases access [ 87 ]. Although Cytoscape's use in FMF research is limited, it presents an opportunity to construct miRNA interaction networks beyond simple differential expression analyses. In RA, Cytoscape has been extensively applied. The molecular complex detection (MCODE) plugin has been used to identify densely connected modules in protein–protein interaction networks, which were then incorporated into competing endogenous RNA (ceRNA) networks linking lncRNAs, miRNAs, and genes [ 86 ]. For instance, one RA study constructed a ceRNA network containing two key lncRNAs, 20 miRNAs, and nine genes, while another built a circRNA–miRNA co‐expression network with 228 interactions, revealing the involvement of apoptosis, inflammation, and autophagy using the GO and KEGG analysis [ 31 ]. Cytoscape's modular design allows customization of network analyses and direct integration of expression or phenotype data onto interaction maps [ 85 , 87 ]. While modern versions can handle large networks, intensive layouts, and clustering can still overwhelm memory or CPU resources, necessitating simplified visualizations or external tools [ 88 ]. Additionally, due to the extensive array of add‐ons, newcomers may find the software challenging to master [ 88 ].