Four-protein model for predicting prognostic risk of lung cancer

Abstract

Patients with lung cancer at the same stage may have markedly different overall outcome and a lack of specific biomarker to predict lung cancer outcome. Heat-shock protein 90 β (HSP90β) is overexpressed in various tumor cells. In this study, the ELISA results of HSP90β combined with CEA, CA125, and CYFRA21-1 were used to construct a recursive partitioning decision tree model to establish a four-protein diagnostic model and predict the survival of patients with lung cancer. Survival analysis showed that the recursive partitioning decision tree could distinguish the prognosis between high- and low-risk groups. Results suggested that the joint detection of HSP90β, CEA, CA125, and CYFRA21-1 in the peripheral blood of patients with lung cancer is plausible for early diagnosis and prognosis prediction of lung cancer.

Keywords

lung cancer / HSP90β / decision tree model / prognosis

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Acknowledgements

This work was granted by CAMS Innovation Fund for Medical Sciences (CIFMS) (No. 2016-I2M-1-001).

Compliance with ethics guidelines

Xiang Wang, Minghui Wang, Lin Feng, Jie Song, Xin Dong, Ting Xiao, and Shujun Cheng declare that they have no conflicts of interest. This manuscript does not involve a research protocol requiring approval by the relevant institutional review board or ethics committee.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at https://doi.org/10.1007/s11684-021-0867-0 and is accessible for authorized users.

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