Clinicopathological features and prognostic nomogram for ovarian cancer based on homologous recombination deficiency status in real world study

Abstract Background Homologous recombination deficiency (HRD) is a critical molecular biomarker in ovarian cancer. However, the clinicopathological characteristics and prognostic implications of HRD in Chinese populations remain inadequately characterized. This study aimed to investigate the clinicopathological differences and prognostic significance between HRD and homologous recombination proficiency (HRP) in ovarian cancer, identify independent risk factors, and develop a nomogram for predicting overall survival (OS) to facilitate individualized clinical decision-making. Methods This study included ovarian cancer patients who underwent surgical treatment at Zhongda Hospital Southeast University between January 2020 and December 2023, with pathologically confirmed diagnosis and completed HRD status testing. Clinicopathological data were extracted from medical records. Prognostic variables were selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression. A nomogram was constructed based on the Cox proportional hazards regression model to predict OS. Model discrimination was assessed using area under the receiver operating characteristic curve (AUC). Calibration curves were used to evaluate agreement between predicted and observed outcomes. Decision curve analysis (DCA) was performed to assess clinical utility. An online version of the nomogram was also developed for clinical translation and individualized risk assessment. Results Significant differences were observed between HRD and HRP groups in multiple clinicopathological parameters, including HE4 levels, operative time, histological type, tumor stage, primary tumor site, p53 expression, and Ki67 expression. Multivariate analysis identified FIGO stage, progesterone receptor expression, and targeted therapy as independent prognostic risk factors, which were incorporated into the final nomogram. The model demonstrated excellent predictive performance and calibration curves showed high consistency between predicted probabilities and actual observations. DCA confirmed significant clinical net benefit across a wide range of threshold probabilities. Conclusions This study elucidates distinct clinicopathological characteristics between ovarian cancer patients with HRD and HRP statuses. The developed nomogram accurately predicts OS in ovarian cancer patients, enabling providing valuable decision support for clinical management.