Mona Sami Awed

Behçet’s disease (BD) is a chronic, multisystem vasculitis characterized by dysregulated immune responses and prominent Th1/Th17 polarization; however, the upstream epigenetic regulatory mechanisms driving this inflammatory imbalance remain incompletely understood. Here, we investigated the coordinated expression and diagnostic relevance of key long non-coding RNAs (lncRNAs; NEAT1 and MEG3) and microRNAs (miR-124 and miR-146a), and their association with IL-17/IL-6-mediated inflammation in BD. In a case–control study, serum levels of the selected non-coding RNAs were quantified by quantitative real-time PCR, while cytokine concentrations were measured using ELISA, complemented by bioinformatic interaction analysis and integrated statistical and machine learning approaches. Patients with BD exhibited marked downregulation of NEAT1, MEG3, miR-124, and miR-146a (p < 0.0001), accompanied by significantly elevated IL-17 and IL-6 levels. Individually, biomarkers demonstrated strong discriminatory capacity (AUC 0.83–0.92), while combined panels further improved classification performance. Multivariate modeling identified these non-coding RNAs as independent predictors of BD, and machine learning analysis identified miR-146a and IL-17 as the most influential contributors to disease classification. Notably, selected biomarkers showed associations with specific clinical manifestations, supporting their potential clinical relevance. Bioinformatic analyses identified putative interactions between NEAT1 and miR-124/miR-146a, while MEG3 demonstrated independent diagnostic value without evidence of direct interaction with the investigated miRNAs. Collectively, these findings provide a hypothesis-generating framework for future mechanistic investigations and support the potential diagnostic value of the investigated biomarker panel, although validation in larger multicenter cohorts and disease-control populations is warranted.

Circular RNAs (circRNAs) are increasingly recognized as significant regulators in multiple cancers, such as hepatocellular carcinoma (HCC), frequently affecting microRNA (miRNA) expression. The diagnostic and prognostic roles of circRNAs, specifically circ_0009910 and circ_0027478, in conjunction with miR-1236-3p, in HCC, have not yet been fully investigated. In this pilot study, we assessed the expression levels of circ_0009910, circ_0027478, and miR-1236-3p in plasma samples from 100 patients diagnosed with HCC and 50 healthy controls through reverse transcriptase–quantitative polymerase chain reaction (RT-qPCR). The diagnostic performance was evaluated using receiver operating characteristic (ROC) curve analysis, and correlations with clinicopathological features were examined. Circ_0009910 and circ_0027478 exhibited significant upregulation in patients with HCC (p < 0.05), whereas miR-1236-3p demonstrated downregulation (p < 0.05). Circ_0009910 demonstrated significant diagnostic accuracy (area under the curve [AUC] = 0.90), effectively differentiating HCC from controls and showing a correlation with tumor size, metastasis, and alpha-fetoprotein (AFP) levels (p < 0.05). Both circ_0009910 and circ_0027478 exhibited a positive correlation with clinicopathological features, whereas miR-1236-3p demonstrated an inverse correlation. Logistic regression validated the diagnostic and prognostic capabilities of these biomarkers. The results indicate that circ_0009910, circ_0027478, and miR-1236-3p, in conjunction with AFP three, present a promising diagnostic and prognostic profile for HCC. Additional validation in larger cohorts is required to establish their clinical utility.