An Externally Validated Predictive Model for Lymph Node Metastasis in Papillary Thyroid Carcinoma Integrating mir-THYpe MicroRNA Signatures and Bioinformatics Features

Introduction: Papillary thyroid carcinoma (PTC) is the most prevalent endocrine malignancy worldwide, with lymph node metastasis (LNM) occurring in 40–60% of patients and representing a critical determinant of disease recurrence. Conventional preoperative risk stratification tools, relying on clinical and ultrasonographic variables, demonstrate insufficient discriminatory performance, particularly in the context of indeterminate cytological findings. Integration of microRNA-based molecular classifiers with bioinformatics frameworks represents a promising but underexplored strategy for improving LNM prediction accuracy. Objective: To develop and validate an integrated LNM risk model for PTC by combining molecular data from the mir-THYpe microRNA classifier with bioinformatics analyses of the TCGA-THCA cohort. Methods: miRNA-seq expression profiles and clinical data from 378 histopathologically confirmed PTC cases (TCGA-THCA) were analyzed using DESeq2. Seven mir-THYpe panel miRNAs differentially expressed between N0 and N1 groups were identified. Target prediction (TargetScan, miRDB, DIANA-TarBase), functional enrichment (DAVID, KEGG, GO), and protein–protein interaction network analyses (STRING, Cytoscape) were performed. LASSO logistic regression with tenfold cross-validation selected six independent predictors, which were incorporated into a multivariate model and nomogram. Model performance was assessed by ROC analysis, Hosmer–Lemeshow calibration, and decision curve analysis, with external validation in GEO cohort GSE60542. Results: The integrated model achieved AUC = 0.841 (training), 0.812 (internal validation), and 0.786 (external validation). The strongest predictors were extrathyroidal extension (OR = 3.84), hsa-miR-146b-5p upregulation (OR = 3.12), and tumor size >1.0 cm (OR = 2.67). Decision curve analysis confirmed superior net clinical benefit over clinical-only and treat-all strategies. Conclusion: Integration of mir-THYpe molecular data with bioinformatics-derived features yielded a well-calibrated, externally validated LNM risk model that outperforms conventional clinical predictors, offering a precision oncology tool for individualized preoperative surgical decision-making in PTC.