IGF2BP2の予後予測における予測値：多施設共同研究

Predictive value of IGF2BP2 in combination with clinicopathological parameters for postoperative recurrence in endometrial cancer (EC): development and validation of a prognostic model.

This multicenter study retrospectively enrolled patients with endometrial cancer who underwent standard surgical treatment between January 2016 and January 2023. The cohort included 545 patients from the First Affiliated Hospital of Chongqing Medical University (training set) and 315 patients from two independent centers-Liangjiang Hospital of Chongqing Medical University and Women and Children's Hospital of Chongqing Medical University (validation set). Univariate and multivariate Cox regression analyses were conducted to identify independent prognostic factors associated with recurrence-free survival (RFS), followed by the development of a nomogram-based prediction model. Model discrimination was evaluated using the area under the receiver operating characteristic curve (AUC), and calibration curves were used to assess the agreement between predicted and observed outcomes. Risk stratification was performed according to nomogram-derived scores, and the clinical applicability of the model was further validated through Kaplan-Meier survival analysis.

Multivariate Cox regression analysis identified International Federation of Gynecology and Obstetrics(FIGO) stage (p=0.001), depth of myometrial invasion (p=0.004), histologic type (p=0.001), CA125 level (p=0.001), p53 status (p=0.013), lymphovascular space invasion (p=0.007), and IGF2BP2 expression (p<0.001) as independent prognostic factors for RFS in endometrial cancer patients. The integrated prediction model incorporating these factors demonstrated excellent performance in predicting 1-, 3-, and 5-year RFS, with significantly superior discriminative ability (AUC = 0.884) compared to single-parameter models.

The nomogram integrating IGF2BP2 with clinicopathological parameters demonstrates robust accuracy for predicting recurrence-free survival in endometrial cancer patients. This tool provides a quantitative risk stratification framework that could potentially contribute to prognostic assessment, though its clinical implementation awaits validation in prospective studies.