Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Apr 15;10(9):e29605.
doi: 10.1016/j.heliyon.2024.e29605. eCollection 2024 May 15.

The predictive value of serum tumor markers for EGFR mutation in non-small cell lung cancer patients with non-stage IA

Affiliations

The predictive value of serum tumor markers for EGFR mutation in non-small cell lung cancer patients with non-stage IA

Wenxing Du et al. Heliyon. .

Abstract

Objective: The predictive value of serum tumor markers (STMs) in assessing epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC), particularly those with non-stage IA, remains poorly understood. The objective of this study is to construct a predictive model comprising STMs and additional clinical characteristics, aiming to achieve precise prediction of EGFR mutations through noninvasive means.

Materials and methods: We retrospectively collected 6711 NSCLC patients who underwent EGFR gene testing. Ultimately, 3221 stage IA patients and 1442 non-stage IA patients were analyzed to evaluate the potential predictive value of several clinical characteristics and STMs for EGFR mutations.

Results: EGFR mutations were detected in 3866 patients (57.9 %) of all NSCLC patients. None of the STMs emerged as significant predictor for predicting EGFR mutations in stage IA patients. Patients with non-stage IA were divided into the study group (n = 1043) and validation group (n = 399). In the study group, univariate analysis revealed significant associations between EGFR mutations and the STMs (carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCC), and cytokeratin-19 fragment (CYFRA21-1)). The nomogram incorporating CEA, CYFRA 21-1, pathology, gender, and smoking history for predicting EGFR mutations with non-stage IA was constructed using the results of multivariate analysis. The area under the curve (AUC = 0.780) and decision curve analysis demonstrated favorable predictive performance and clinical utility of nomogram. Additionally, the Random Forest model also demonstrated the highest average C-index of 0.793 among the eight machine learning algorithms, showcasing superior predictive efficiency.

Conclusion: CYFRA21-1 and CEA have been identified as crucial factors for predicting EGFR mutations in non-stage IA NSCLC patients. The nomogram and 8 machine learning models that combined STMs with other clinical factors could effectively predict the probability of EGFR mutations.

Keywords: Epidermal growth factor receptor; Lung cancer; Machine learning; Nomogram model; Serum tumor markers.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Flow chart of the study design and analysis.
Fig. 2
Fig. 2
Distribution of EGFR mutation subtypes in all NSCLC patients.
Fig. 3
Fig. 3
Construction and validation of the nomogram predictive model. (A) The Nomogram model for predicting EGFR mutations in the study cohort. (B) ROC curves for the nomogram model in differentiating EGFR mutation status; (C) DCA curves to evaluate the clinical utility of the nomogram model for predicting EGFR mutations. (D) ROC curves for the discrimination of the nomogram; (E) The calibration plot in the study cohort; (F) The calibration plot in the validation cohort. Pr (EGFR): Probability of EGFR Mutation; ADC, adenocarcinoma; **means p < 0.01, ***means p < 0.001, ROC, receiver operating characteristic; DCA, decision curve analysis; AUC, area under the curve.
Fig. 4
Fig. 4
ROC curves for 8 machine learning models in predicting EGFR mutations. (A) ROC curves in the study cohort; (B) ROC curves in the validation cohort; (C) A total of 8 kinds of prediction models and further calculated the C-index of each model. ROC, receiver operating characteristic; AUC, area under the curve; RF, Random Forest; GBM, Gradient Boosting Machine; NNET, Neural Network; SVM, Support Vector Machines; LASSO, Lasso Regression algorithm; GLM, Generalized Linear Model; KNN, K-Nearest Neighbor; LR, Logistic Regression.

Similar articles

References

    1. Siegel R.L., Miller K.D., Wagle N.S., Jemal A. Cancer statistics. CA A Cancer J. Clin. 2023;73(1):17–48. 2023. - PubMed
    1. Sung H., Ferlay J., Siegel R.L., Laversanne M., Soerjomataram I., Jemal A., Bray F. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA A Cancer J. Clin. 2021;71(3):209–249. - PubMed
    1. Herbst R.S., Heymach J.V., Lippman S.M. Lung cancer. N. Engl. J. Med. 2008;359(13):1367–1380. - PMC - PubMed
    1. Marin-Acevedo J.A., Pellini B., Kimbrough E.O., Hicks J.K., Chiappori A. Treatment strategies for non-small cell lung cancer with common EGFR mutations: a review of the history of EGFR TKIs approval and emerging data. Cancers. 2023;15(3) - PMC - PubMed
    1. Shi Y., Au J.S., Thongprasert S., Srinivasan S., Tsai C.M., Khoa M.T., Heeroma K., Itoh Y., Cornelio G., Yang P.C. A prospective, molecular epidemiology study of EGFR mutations in Asian patients with advanced non-small-cell lung cancer of adenocarcinoma histology (PIONEER) J. Thorac. Oncol. 2014;9(2):154–162. - PMC - PubMed