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Four-protein model for predicting prognostic risk of lung cancer

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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.

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Acknowledgements

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

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Authors and Affiliations

  1. State Key Laboratory of Molecular Oncology, Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China

    Xiang Wang, Minghui Wang, Lin Feng, Jie Song, Ting Xiao & Shujun Cheng

  2. Department of Clinical Laboratory, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China

    Xin Dong

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  1. Xiang Wang
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  2. Minghui Wang
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  4. Jie Song
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  5. Xin Dong
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  6. Ting Xiao
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  7. Shujun Cheng
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Correspondence to Xin Dong, Ting Xiao or Shujun Cheng.

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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.

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Wang, X., Wang, M., Feng, L. et al. Four-protein model for predicting prognostic risk of lung cancer. Front. Med. 16, 618–626 (2022). https://doi.org/10.1007/s11684-021-0867-0

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  • DOI: https://doi.org/10.1007/s11684-021-0867-0

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