Xu Qian, Wu Xue, Guo Yabin
Department of Gynecology, Changde Hospital, Xiangya School of Medicine, Central South University (The First People's Hospital of Changde City) Changde 415000, Hunan, China.
Center of Reproductive Medicine, Changde Hospital, Xiangya School of Medicine, Central South University (The First People's Hospital of Changde City) Changde 415000, Hunan, China.
Am J Cancer Res. 2025 Aug 15;15(8):3376-3394. doi: 10.62347/ZZPA6435. eCollection 2025.
To investigate the synergistic interaction between polycystic ovary syndrome (PCOS) and metabolic syndrome (MetS) in relation to the risk of endometrial cancer (EC). Additionally, we aimed to develop a clinically applicable, high-risk early-warning model that incorporates these interactive factors, enhancing the precision and clinical utility of EC screening.
We conducted a retrospective case-control study involving 445 newly diagnosed EC patients and 299 healthy female controls from the First People's Hospital of Changde City, between January 2018 and January 2025. Multivariate logistic regression was used to assess the independent and combined effects of PCOS and MetS on EC risk. A nomogram-based predictive model was developed and validated rigorously using training, internal validation, and external validation cohorts. The model's performance was evaluated based on discrimination (area under the curve [AUC]), calibration (Hosmer-Lemeshow test), and clinical utility (decision curve analysis). The diagnostic performance of our comprehensive model was compared to traditional tumor markers (cancer antigen 125/199, human epididymis protein 4).
LASSO regression identified 14 clinically significant predictors. Logistic regression revealed that HE4 levels, endometrial thickness, and fasting blood glucose were independent risk factors for EC, while high-density lipoprotein was an independent protective factor. The nomogram based on these variables demonstrated excellent discrimination, with AUCs of 0.984 in the training set, 0.987 in the internal validation set, and 0.964 in the external validation set. The integrated risk model significantly outperformed individual markers in diagnostic accuracy across all datasets (P<0.001).
Our PCOS-MetS interaction-based EC risk prediction model showed robust and consistent performance across multiple validation cohorts. This tool significantly improves early detection accuracy and holds substantial clinical promise, laying the foundation for personalized EC risk management strategies.
探讨多囊卵巢综合征（PCOS）与代谢综合征（MetS）之间的协同相互作用与子宫内膜癌（EC）风险的关系。此外，我们旨在开发一种临床适用的高危预警模型，该模型纳入这些相互作用因素，提高EC筛查的准确性和临床实用性。
我们进行了一项回顾性病例对照研究，纳入了2018年1月至2025年1月期间常德市第一人民医院的445例新诊断的EC患者和299例健康女性对照。采用多因素逻辑回归评估PCOS和MetS对EC风险的独立和联合影响。开发了基于列线图的预测模型，并使用训练、内部验证和外部验证队列进行了严格验证。基于辨别力（曲线下面积[AUC]）、校准（Hosmer-Lemeshow检验）和临床实用性（决策曲线分析）对模型性能进行评估。将我们综合模型的诊断性能与传统肿瘤标志物（癌抗原125/199、人附睾蛋白4）进行比较。
LASSO回归确定了14个具有临床意义的预测因子。逻辑回归显示，HE4水平、子宫内膜厚度和空腹血糖是EC的独立危险因素，而高密度脂蛋白是独立保护因素。基于这些变量的列线图显示出优异的辨别力，训练集的AUC为0.984，内部验证集为0.987，外部验证集为0.964。在所有数据集中，综合风险模型在诊断准确性方面显著优于单个标志物（P<0.001）。
我们基于PCOS-MetS相互作用的EC风险预测模型在多个验证队列中表现出稳健且一致的性能。该工具显著提高了早期检测准确性，具有巨大的临床应用前景，为个性化EC风险管理策略奠定了基础。
