Zhang YN, Jiang T, Zhang PJ, Wang HJ. Construction and validation of a multiparameter diagnostic model based on conventional tumor markers and cytokines for lung cancer. World J Clin Oncol 2026; 17(4): 119365 [DOI: 10.5306/wjco.v17.i4.119365]
Corresponding Author of This Article
Hui-Juan Wang, MD, Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Beijing Institute of Respiratory Medicine, Capital Medical University, No. 8 Gongti South Road, Beijing 100020, China. 13466791738@163.com
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Respiratory System
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Observational Study
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Apr 24, 2026 (publication date) through Apr 22, 2026
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World Journal of Clinical Oncology
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2218-4333
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
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Zhang YN, Jiang T, Zhang PJ, Wang HJ. Construction and validation of a multiparameter diagnostic model based on conventional tumor markers and cytokines for lung cancer. World J Clin Oncol 2026; 17(4): 119365 [DOI: 10.5306/wjco.v17.i4.119365]
World J Clin Oncol. Apr 24, 2026; 17(4): 119365 Published online Apr 24, 2026. doi: 10.5306/wjco.v17.i4.119365
Construction and validation of a multiparameter diagnostic model based on conventional tumor markers and cytokines for lung cancer
Yi-Ning Zhang, Tao Jiang, Peng-Jun Zhang, Hui-Juan Wang
Yi-Ning Zhang, Hui-Juan Wang, Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Beijing Institute of Respiratory Medicine, Capital Medical University, Beijing 100020, China
Tao Jiang, Division of Medicine Innovation Research, Chinese PLA General Hospital, Beijing 100853, China
Peng-Jun Zhang, Department of Interventional Therapy, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital and Institute, Beijing 100142, China
Co-first authors: Yi-Ning Zhang and Tao Jiang.
Co-corresponding authors: Peng-Jun Zhang and Hui-Juan Wang.
Author contributions: Wang HJ and Zhang PJ designed the study and they contribute equally to this study as co-corresponding authors; Jiang T, Zhang PJ and Wang HJ performed the research; Jiang T and Zhang YN analyzed the data; Wang HJ wrote the paper; Zhang PJ and Wang HJ revised the manuscript for final submission; Zhang YN and Jiang T contribute equally to this study as co-first authors.
Supported by the National Key Research and Development Program of China, No. 2020YFC2004604.
Institutional review board statement: The study was reviewed and approved by the Peking University Cancer Hospital & Institute Review Board (Approval No. 2023KT37).
Informed consent statement: This study was approved by the Ethics Committee with a waiver of informed consent.
Conflict-of-interest statement: We declare that we have no financial or personal relationships with other individuals or organizations that can inappropriately influence our work and that there is no professional or other personal interest of any nature in any product, service and/or company that could be construed as influencing the position presented in or the review of the manuscript.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Data sharing statement: There were no data to share.
Corresponding author: Hui-Juan Wang, MD, Department of Respiratory and Critical Care Medicine, Beijing Chao-Yang Hospital, Beijing Institute of Respiratory Medicine, Capital Medical University, No. 8 Gongti South Road, Beijing 100020, China. 13466791738@163.com
Received: January 30, 2026 Revised: February 10, 2026 Accepted: March 6, 2026 Published online: April 24, 2026 Processing time: 81 Days and 1.4 Hours
Abstract
BACKGROUND
Early detection of lung cancer is urgently needed in clinical practice.
AIM
To evaluate the diagnostic value of conventional tumor markers and cytokines for lung cancer and construct a multiparameter diagnostic model lung cancer detection.
METHODS
A total of 152 healthy controls and 113 lung cancer patients were included in the model. In addition, 21 healthy controls and 36 lung cancer patients were separately included to validate the model. Three conventional tumor markers and 10 cytokines were detected. Four multiparameter joint analysis methods, binary logistic regression analysis, discriminant analysis, a classification tree and a neural network, were used to establish and compare multiparameter joint diagnosis models.
RESULTS
Six differentially expressed indicators [carcinoembryonic antigen (CEA), cytokeratin 19 fragment (CY211), neuron-specific enolase, interleukin (IL)-8, monocyte chemoattractant protein-1, and tumor necrosis factor-alpha (TNF-α)] were screened out, among which IL-8 [area under the curve (AUC) = 0.957] and TNF-α (AUC = 0.936) had the optimal diagnostic efficacy. The binary logistic regression model was chosen as the optimal multiparameter combined auxiliary diagnostic model. When 152 healthy controls and 113 lung cancer s were differentiated via the model, the AUC was 0.980. After validation, when 21 healthy controls and 36 lung cancer patients were distinguished, the AUC was 0.922, indicating good stability and superior performance to that of CEA alone.
CONCLUSION
We constructed a multiparameter binary logistic regression diagnostic model that included CEA, CY211, IL-8 and TNF-α for the auxiliary detection of lung cancer. Compared with conventional CEA, it significantly improved diagnostic accuracy.
Core Tip: We aimed to evaluate the diagnostic value of conventional tumor markers and cytokines and construct a multiparameter diagnostic model. After four multiparameter joint analysis methods analysis, a model that included carcinoembryonic antigen, cytokeratin 19 fragment, interleukin-8 and tumor necrosis factor-alpha for the detection of lung cancer was built and validated. It may provide more comprehensive theoretical support for the diagnosis and targeted therapy of lung cancer.
