Construction of cuproptosis-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics
10.3969/j.issn.1001-3733.2025.02.018
- VernacularTitle:基于生物信息学构建铜死亡相关口腔鳞癌预后模型
- Author:
Baixin GAO
1
;
Ling LI
;
Jingfei ZHANG
;
Chao YUAN
;
Meng ZHANG
;
Zhen CAI
Author Information
1. 121000,锦州医科大学临沂市人民医院研究生培养基地
- Publication Type:Journal Article
- Keywords:
Cuproptososis;
Oral squamous cell carcinoma;
Prognostic model;
Bioinformatics
- From:
Journal of Practical Stomatology
2025;41(2):253-260
- CountryChina
- Language:Chinese
-
Abstract:
Objective:The transcriptome data was utilized to screen cuproptosis-related genes(CRGs)in oral squamous cell car-cinoma(OSCC),and the characteristic genes were identified for constructing a prognostic model for predicting patients'survival time.Methods:OSCC transcriptome gene expression and clinical data were obtained from TCGA and GEO.Through Lasso regres-sion analysis and Cox regression analysis,relevant prognostic genes were screened and prognostic models were constructed.Ac-cording to the median value of risk scores,patients were divided into high and low risk groups,and their survival rates were com-pared.Finally,the predictive performance of the model was verified.Results:In this study,9 characteristic genes with prognostic value(ENO2,P4HA1,SLC2A3,AQP1,PLS1,NXPH4,CTSG,TRAC,THBS1)were screened out and a 9-gene prognostic model was constructed.The survival rate of high-risk group based on prognostic model was significantly lower than that of low-risk group.The area under curve(AUC)of receiver operating characteristic curve(ROC)was 0.701,0.729 and 0.702 at 1 year,3 years and 5 years,respectively,which verifies that the risk model has good predictive performance.The nomogram predicted that the 1-year,3-year,and 5-year survival probabilities of OSCC patients are 89.6％,72.4％,and 63.9％respectively,and the cal-ibration curve confirmed the accuracy of the nomogram prediction.Conclusion:The 9-gene prognostic model based on CRGs screening could predict the prognosis of OSCC patients,which is helpful for clinical personalized treatment of OSCC patients and prediction of their survival rate.
