Interpretable miRNA-based prediction model for early detection of pancreatic cancer: Development and cross-platform validation - Yanfei Zhu; Linglin Zhu; Yumei Liu; Yongshuo Ji; Junqiu Zhu; Hong Zhao (PLoS ONE, 2026)
Background Pancreatic cancer remains one of the most lethal malignancies, largely due to delayed diagnosis. Although microRNA (miRNA) biomarkers show promise, many previous studies lack cross-platform validation and model interpretability, limiting clinical applicability. Methods We developed and externally validated an interpretable diagnostic model based on a 20-miRNA signature using publicly available datasets. A total of 801 samples were included, of which 767 were used for model training and validation. The training cohort comprised GSE59856 and GSE85589 (n = 216), and independent validation cohorts included TCGA-PAAD and GTEx pancreas (n = 585), with additional serum-based validation (GSE128508; n = 30). Feature selection and model development were conducted exclusively within the training cohort. A Random Forest classifier was applied, and model interpretability was assessed using SHAP analysis. Diagnostic performance was evaluated using cross-validation and independent external validation. Results The model achieved a cross-validation AUC of 0.87 (95% CI 0.82–0.92), with sensitivity of 84.7% and specificity of 83.1% in the training cohort. External validation across independent RNA-seq and qRT-PCR datasets demonstrated AUC values ranging from 0.78 to 0.83. Performance remained broadly consistent across sample types and platforms. SHAP analysis identified miR-6875-5p, miR-196a-5p, and miR-1246 among the principal contributors to classification. Functional enrichment analysis suggested involvement in canonical cancer-related pathways. Conclusions We developed and externally validated an interpretable 20-miRNA signature for pancreatic cancer diagnosis with consistent performance across independent cohorts. Although based on retrospective datasets, the structured validation strategy and explainable modeling framework provide a transparent foundation for future prospective evaluation.