A Novel Diagnostic and Subtype Classification Model Based on RNA N6-Methyladenosine Regulators for Behçet's Uveitis

To investigate the role of RNA N6-methyladenosine (m⁶A) regulators in the diagnosis and subtype classification of Behçet's uveitis, aiming to establish a novel predictive model and explore distinct molecular patterns for personalized therapeutic approaches.

Data from the Gene Expression Omnibus GSE209567 dataset comprising 22 Behçet's uveitis patients and 15 controls were analyzed for m⁶A regulator expression. Differentially expressed genes were identified using the "limma" R package, followed by random forest (RF) and support vector machine (SVM) model construction to select critical m6A regulators. A nomogram model was developed for prediction, and consensus clustering identified distinct m⁶A and gene-regulating patterns. Immune cell infiltration analysis was conducted using ssGSEA, and m⁶A scores were computed to quantify molecular patterns.

Eleven m⁶A regulators were significantly differentially expressed. The top four candidate m⁶A regulators (FTO, YTHDF2, CBLL1, and METTL14) were identified to predict the risk of Behçet's uveitis. A nomogram was constructed based on the four candidate m⁶A regulators to visualize the association between the expression levels of the candidate with the risk of onset of Behçet's uveitis. Two distinct m⁶A patterns and gene patterns were identified, validated by consensus clustering. High m⁶A scores were associated with more severe disease stages, with differential immune cell infiltration observed between subtypes. Immune-related genes, such as LRRN3 and DAAM2, were identified as key in differentiating m⁶A patterns.

M⁶A modification plays an important role in the occurrence of uveitis. Distinct m⁶A patterns and gene clusters highlight their potential for early diagnosis and personalized treatment.