Evidence map›Paper›PMID 39789507›Full record

ArticleBMC cancer2025

CK19 protein expression: the best cutoff value on the prognosis and the prognosis model of hepatocellular carcinoma.

Chenglei Yang, Wanyan Xiang, Zongze Wu, Nannan Li, Guoliang Xie, Juntao Huang, Lixia Zeng, Hongping Yu, Bangde Xiang

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Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

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5citing papers in PubMed
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3 · Its place in the literature

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5 citing papers in PubMed.

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5 · Who and what money

Authors and funding

9 authors.

Chenglei Yang *Department of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Province, 530021, China.
Wanyan Xiang *The First Clinical Medical College of Guangxi Medical University, Nanning, Guangxi Province, 530021, China.
Zongze Wu *The First Clinical Medical College of Guangxi Medical University, Nanning, Guangxi Province, 530021, China.
Nannan Li *Department of Ultrasound, Guangxi Zhuang Autonomous Region Workers' Hospital, Nanning, Guangxi Province, 530021, China.
Guoliang XieDepartment of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Province, 530021, China.
Juntao HuangDepartment of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Province, 530021, China.
Lixia ZengDepartment of Pathology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Province, 530021, China.
Hongping YuRegional Key Laboratory for Early Prevention and Treatment of High Incidence Tumor, Ministry of Education, Nanning, Guangxi Province, 530021, China. yhp268@163.com.
Bangde XiangDepartment of Hepatobiliary Surgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi Province, 530021, China. Xiangbangde@gxmu.edu.cn.

Funding

Guangxi Key Research and Development Program AA18221001, AB18050020High-level Innovation Team and Outstanding Scholar Program in Guangxi Colleges and Universities, Youth Science Foundation of Guangxi Medical University GXMUYSF202407Key Laboratory of Early Prevention and Treatment for Regional High-Frequency Tumor, Ministry of Education/Guangxi, Independent Research Project GKE-ZZ202215National Natural Science Foundation of China 82260573/81960450
6 · The paper itself

Abstract

BACKGROUND AND

objectiveIn clinical practice, CK19 can be an important predictor for the prognosis of HCC. Due to the high incidence and mortality rates of HCC, more effective and practical prognostic prediction models need to be developed urgently.

methodsA total of 1,168 HCC patients, who underwent radical surgery at the Guangxi Medical University Cancer Hospital, between January 2014 and July 2019, were recruited, and their clinicopathological data were collected. Among the clinicopathological data, the optimal cutoff value of CK19-positive HCC was determined by calculating the area under the curve (AUC) using survival analysis and time-dependent receiver operating characteristic (timeROC) curve analysis. The predictors were screened using univariate and multivariate COX regression and least absolute shrinkage and selection operator (LASSO) regression to construct nomogram prediction models, and their predictive potentials were assessed using calibration curves and AUC values.

resultsThe 0% positive rate of CK19 was considered the optimal cutoff value to predict the poor prognosis of CK19-positive HCC. The survival analysis of 335 CK19-positive HCC showed no significant statistical differences in the overall survival (OS) and disease-free survival (DFS) of CK19-positive HCC patients. A five-factor risk (CK19, CA125, Edmondson, BMI, and tumor number) scoring model and an OS nomograph model were constructed and established, and the OS nomograph model showed a good predictive performance and was subsequently verified.

conclusionA 0% expression level of CK19 protein may be an optimal threshold for predicting the prognosis of CK19-positive HCC. Based on this, CK19 marker a good nomogram model was constructed to predict HCC prognosis.

Indexed as

Biomarkers, TumorCarcinoma, HepatocellularKeratin-19Liver NeoplasmsNomogramsAdultAgedDisease-Free SurvivalFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesROC CurveBiomarkers, TumorKeratin-19KRT19 protein, humanCytokeratin 19Hepatocellular carcinoma (HCC)Nomogram modelOptimal cut-off valuePrognosis

Identifiers

PMID39789507
PMCPMC11720332

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