Construction of a nomogram prediction model for the efficacy of EGFR-TKI targeted therapy in advanced lung adenocarcinoma with EGFR mutation based on lung cancer autoantibodies
10.19405/j.cnki.issn1000-1492.2025.07.023
- VernacularTitle:基于肺癌自身抗体的 EGFR 突变晚期肺腺癌一代EGFR - TKI 靶向治疗效果的列线图预测模型构建
- Author:
Linge Sun
1
;
Jiao Su
1
;
Yanjun Liu
1
;
Liping Dai
1
;
Ruiying Chen
1
;
Songyun Ouyang
1
Author Information
1. Dept of Respiratory and Sleep Medicine，The First Affiliated Hospital of Zhengzhou University，Zhengzhou 450052
- Publication Type:Journal Article
- Keywords:
lung cancer autoantibodies;
epidermal growth factor receptor mutation;
advanced lung adenocarcinoma;
targeted therapy;
nomogram model;
EGFR-TKI
- From:
Acta Universitatis Medicinalis Anhui
2025;60(7):1325-1332
- CountryChina
- Language:Chinese
-
Abstract:
Objective :To explore the factors influencing the efficacy of first-generation EGFR tyrosine kinase inhibitors(EGFR-TKIs) in patients with EGFR-mutated advanced lung adenocarcinoma and to construct and validate a corresponding nomogram prediction model.
Methods :A total of 220 patients with EGFR-mutated advanced lung adenocarcinoma treated with icotinib were enrolled and randomly divided into a training group(154 cases) and a validation group(66 cases) in a 7 ∶3 ratio. Cox regression analysis was performed to identify factors affecting the efficacy of first-generation EGFR-TKIs in the training group. A prediction model was constructed, and calibration curves and receiver operating characteristic(ROC) curves were plotted to validate the model′s performance.
Results:In the training group, the objective response rate was 35.71%, the disease control rate was 77.27%, the median progression-free survival(PFS) was 12.5 months, the median overall survival was 18 months, the 2-year OS rate was 66.23%, and the PFS rate was 42.21%. Univariate analysis showed that brain metastasis, bone metastasis, TNM stage, differentiation degree, neutrophil-to-lymphocyte ratio(NLR), post-treatment p53 levels, p53 difference(Δp53), post-treatment cancer antigen gene(CAGE) levels, and CAGE difference(ΔCAGE) were associated with PFS(P2=4.429, P=0.351). ROC curve analysis in the training group showed that the nomogram model had a sensitivity of 80.00%, specificity of 77.53%, and AUC of 0.864 for predicting therapeutic efficacy, while the validation group showed a sensitivity of 74.08%, specificity of 71.43%, and AUC of 0.835.
Conclusion:Changes in lung cancer autoantibodies(Δp53 and ΔCAGE), TNM stage, and NLR are key factors influencing the efficacy of first-generation EGFR-TKIs in EGFR-mutated advanced lung adenocarcinoma. The nomogram prediction model based on p53 and CAGE demonstrates good predictive performance.
- Full text:2026041423174053816基于肺癌自身抗体的EGFR突变晚期肺腺癌一代EGFR-TKI靶向治疗效果的列线图预测模型构建_孙琳歌.pdf
