Four-protein model for predicting prognostic risk of lung cancer.
10.1007/s11684-021-0867-0
- Author:
Xiang WANG
1
;
Minghui WANG
1
;
Lin FENG
1
;
Jie SONG
1
;
Xin DONG
2
;
Ting XIAO
3
;
Shujun CHENG
4
Author Information
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.
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. dongxin_ab@163.com.
3. 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. xiaot@cicams.ac.cn.
4. 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. chengshj@cicams.ac.cn.
- Publication Type:Journal Article
- Keywords:
HSP90β;
decision tree model;
lung cancer;
prognosis
- MeSH:
Antigens, Neoplasm;
Biomarkers, Tumor;
CA-125 Antigen;
Carcinoembryonic Antigen;
Carcinoma, Non-Small-Cell Lung/pathology*;
Humans;
Keratin-19;
Lung Neoplasms;
Prognosis
- From:
Frontiers of Medicine
2022;16(4):618-626
- CountryChina
- Language:English
-
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.
