Development and validation of a miRNA-based prognostic model for high-grade serous ovarian cancer: a retrospective cohort study

Background Ovarian cancer, particularly high-grade serous ovarian cancer (HGSOC), is the most lethal gynecological malignancy, mainly due to late-stage diagnosis and limited prognostic biomarkers. Current clinical markers, such as CA125, have limited prognostic accuracy for risk stratification. MicroRNAs (miRNAs) have emerged as promising biomarkers due to roles in tumor biology and stability in biofluids. This study aimed to identify and validate prognostic miRNA biomarkers in HGSOC. Methods A machine learning pipeline was implemented to develop a prognostic model using miRNA data. Candidate miRNAs were identified through feature selection and differential expression analyses. Recursive Feature Elimination determined the optimal predictor set among miRNAs combined with age, stage, and MUC16 . Model development used a hold-out split, with hyperparameter optimization under 5-fold cross-validation within training. Performance was evaluated using area under the receiver operating characteristic curve (AUC), recall, and balanced accuracy. SHAP analysis assessed feature contributions, while enrichment analyses characterized miRNA–mRNA interactions and pathways. Findings The final model, incorporating 9 miRNAs with clinical variables, achieved an AUC of 0.762 95% CI: 0.621–0.903, exceeding previously reported signatures. Key miRNAs, including hsa-miR-205-5p and hsa-miR-150-5p, were associated with angiogenesis, invasion, and chemoresistance pathways. In RT-qPCR validation, discriminative performance decreased; however, the continuous risk score remained independently associated with survival, achieving a C-index of 0.85 in multivariable analysis. Interpretation We present an interpretable miRNA-based prognostic model for HGSOC integrating molecular features. Although ROC-based discrimination was limited in external validation, survival analyses supported independent prognostic value, with the continuous risk score significantly associated with survival. Continuous and classification-based stratification identified survival groups, supporting clinical relevance of the model and identified miRNA signature. Funding CNPq; FAPERJ; Brazilian Ministry of Health (INCA/MS).