Construction and evaluation of a prognostic model for hepatocellular carcinoma based on disulfidptosis-related lncRNA
Objective To construct a disulfidptosis-related long non-coding RNA(DRlncRNA)prognostic model for hepatocellular carcinoma(HCC)based on the Cancer Genome Atlas(TCGA)database.Methods Based on the transcriptome data and clinical information of 377 HCC patient samples(including 374 HCC tissues and 50 paracancerous tissues)downloaded from TCGA,DRlncRNAs were identified by Pearson correlation analysis on disulfidptosis-related genes and long non-coding RNA(lncRNA).The 343 samples screened were randomly grouped into a training set(172 cases)and a validation set(171 cases)by 1∶1.In the training set,DRlncRNA prognostic risk models were determined by univariable Cox regression analyses,LASSO regression,and multivariable Cox regression analyses,risk scores were calculated,and the patients were divided into high-and low-risk groups according to the median value of the risk scores;Kaplan-Meier method was used for the survival analysis of patients in the two groups.ROC curves were plotted to test the efficacy of the prognostic risk model.Validation was repeated in the validation set and in the entire cohort.Differences in immune characteristics between high-and low-risk groups were analyzed.Results Through Pearson correlation analysis,a total of 185 DRlncRNAs were screened,and six were finally determined by multivariable Cox regression analysis to construct the prognostic model.Kaplan-Meier survival analysis showed that patients in the high-risk group in all three cohorts had significantly shorter overall survival than those in the low-risk group(all P＜0.05).ROC curves showed that the model had a good prognostic predictive efficacy in the training set of HCC patients with AUCs of 0.806,0.794,and 0.694 at 1,3,and 5 years,respectively;in the validation set and the entire cohort,the model also showed good prediction efficacy.Further analyses showed that immune cells and functions differed to different degrees in the high-and low-risk groups.Conclusion A prognostic model constructed on the basis of 6 DRlncRNAs can effectively predict the prognosis of HCC patients.High risk score is an independent risk factor for HCC.
万方数据
维普
