Integrative prognostic modeling of ovarian cancer: incorporating genetic, clinical, and immunological markers.

Abstract

Ovarian cancer has a high mortality rate, primarily due to late diagnosis and complex pathogenesis. This study develops an integrative prognostic model combining genetic, clinical, and immunological data to predict outcomes in ovarian cancer patients. Utilizing data from The Cancer Genome Atlas (TCGA), we identified significant prognostic genes through differential expression and survival analysis, integrating these with clinical features and immune landscape assessments including immune cell infiltration and checkpoint expression. The risk score effectively predicted patient survival, distinguishing between high and low-risk groups with significant outcome differences. High-risk patients demonstrated poor prognosis, greater immune checkpoint expression, and higher tumor mutational burdens (TMB), suggesting potential responsiveness to immunotherapy. The model's predictive capacity was validated across multiple cohorts, showing consistent performance in survival prediction and treatment response. Calibration curves and decision curve analysis confirmed the model's clinical utility. This study highlights the potential of an integrated approach to enhance personalized treatment strategies in ovarian cancer, aiming to improve patient management and outcomes.