Transcription Factor Forkhead Box Protein 3 (FOXP3) as a Prognostic Indicator for Postoperative Outcomes in Patients with Breast Cancer: Establishment of a Prognostic Nomogram

转录因子叉头框蛋白 3 (FOXP3) 作为乳腺癌患者术后结果的预后指标:建立预后列线图

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作者:Chunlei Tan, Jinling Xu, Shiyuan Zhang, Shuqiang Liu, Xiaotian Yang, Danping Wu, Boqian Yu, Yuanxi Huang

Conclusion

FOXP3 expression serves as a crucial prognostic indicator in breast cancer patients, and may aid preoperative evaluation in clinical practice.

Methods

A cohort of 313 cases between January 2015 and November 2015 were enrolled this research. Immunohistochemistry (IHC) assay was utilized to detect the expression levels of FOXP3 in primary breast carcinoma specimens. These patients were separated into two groups by semiquantitative scoring approach. Chi-square test and Fisher's exact test were conducted to investigate the correlations between FOXP3 expression in tumors and clinicopathological variables. Kaplan-Meier method and Log rank test were utilized to generate survival curves for disease-free survival (DFS) and overall survival (OS). The independent factors were examined using Cox regression analysis. Nomogram models were created for assessing DFS and OS rates.

Purpose

The current investigation is to assess FOXP3 expression in breast cancer patients and evaluate the predictive significance of FOXP3. Patients and

Results

Depending on the levels of FOXP3 expression in tumors, these patients were categorized into two groups: low FOXP3 expression (174 cases) and high FOXP3 expression (139 cases). The patients exhibiting low levels of FOXP3 expression in tumors demonstrated a longer survival duration contrasted with those with high expression (DFS: 88.75 vs 65.87 months, χ2=36.1100, P<0.0001; OS: 89.70 vs 78.37 months, χ2=32.4900, P<0.0001). Multivariate analysis revealed that FOXP3 was a significant prognostic factor [DFS: hazard ratio (HR): 2.822, 95% CI: 1.595-4.992, P<0.0001; OS: HR: 3.232, 95% CI: 1.812-5.763, P<0.0001]. The good predictive clinical utility of FOXP3-based nomograms within the threshold probability range for different survival rates was demonstrated by calibration curve and decision curve analyses.

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