Correlation of Serum sIL-2R, VEGF, and ES with Estrogen Levels in Papillary Thyroid Carcinoma Patients and Their Predictive Value for Postoperative Recurrence
Abstract
Objective: To explore the correlation of serum soluble interleukin-2 receptor (sIL-2R), vascular endothelial growth factor (VEGF), and endostatin (ES) with estrogen levels in papillary thyroid carcinoma (PTC) patients, and to assess the predictive efficacy of these biomarkers for PTC diagnosis and postoperative recurrence.
Methods: From March 2023 to March 2024, 132 newly diagnosed PTC patients and 128 healthy controls were enrolled. Serum sIL-2R, VEGF, and ES levels were quantified using enzyme-linked immunosorbent assay (ELISA), while estrogen levels (estrone [E1], estradiol [E2], estriol [E3]) were measured via chemiluminescent immunoassay. Patients were followed postoperatively for one year to monitor recurrence events, including local recurrence, lymph node metastasis, and distant metastasis. The diagnostic performance of the combined model was evaluated using receiver operating characteristic (ROC) curve analysis, and Pearson correlation analysis was conducted to examine the relationship between biomarkers and estrogen levels.
Results: Compared to controls, PTC patients exhibited significantly elevated serum sIL-2R, VEGF, and ES levels (P<0.05). The combined detection of these biomarkers demonstrated a sensitivity of 80.30% and specificity of 78.91% (AUC=0.8526) for PTC diagnosis. Additionally, E1 and E2 levels were significantly higher in PTC patients (P<0.05) and showed positive correlations with sIL-2R, VEGF, and ES (P<0.05), whereas E3 levels changed insignificantly (P>0.05). Recurrent patients had significantly higher sIL-2R, VEGF, and ES levels than non-recurrent patients (P<0.05). The combined predictive model for recurrence achieved a sensitivity of 96.88% and specificity of 61.00% (AUC=0.8494).
Conclusion: Elevated serum sIL-2R, VEGF, and ES levels in PTC patients indicate that their combined assessment may serve as a sensitive and specific tool for PTC diagnosis and postoperative recurrence risk stratification.
Copyright (c) 2025 Yan Guo, Xin Ci, Shaoyu Han, Jianli Cui
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