精准医学杂志2025，Vol.40Issue(6)：499-504,6.DOI:10.13362/j.jpmed.202540110
基于缺氧相关基因的肝细胞癌预后预测模型的构建与验证
Establishment and validation of a predictive model for the prognosis of hepatocellular carcinoma based on hypoxia-related genes
摘要
Abstract
Objective To establish a predictive model for the prognosis of hepatocellular carcinoma(HCC)based on hy-poxia-related genes,and to provide a new method for prognostic evaluation in clinical practice.Methods Genetic data and clini-cal survival time were collected from 368 HCC patients in the TCGA-LIHC dataset.R language was used to analyze the differential-ly expressed genes(DEGs)associated with HCC in the genetic data,and the protein-protein interaction(PPI)network was used to identify hypoxia-related genes from the above DEGs.Based on the clinical survival time of patients,univariate Cox regression analy-sis and LASSO regression analysis were used to further identify the genes affecting the survival prognosis of HCC patients among the above hypoxia-related genes,and a predictive model was established for the prognosis of hypoxia-driven HCC.The 368 HCC patients from the TCGA-LIHC dataset were established as the training set,and 352 HCC patients from the GSE14520 dataset and the ICGC-JP dataset were established as the validation set.The patients in the training set and the validation set were divided into high prognostic risk group and low prognostic risk group based on the predictive model for the prognosis of HCC.The Kaplan-Meier survival curve was used for comparison of overall survival rate between the two groups of patients in the training set and the valida-tion set;the receiver operating characteristic(ROC)curve was used to assess the performance of the model in the training set and the validation set;the calibration curve was used to validate the predictive ability of the model in the validation set.RT-qPCR was used to measure the expression levels of hypoxia-related HCC genes in human liver cancer cell lines HEPG2 and HUH7 under hy-poxic conditions.Results A total of 12 645 HCC-related DEGs were obtained from the TCGA-LIHC dataset by R language,among which 20 hypoxia-related genes were identified by the PPI network.The univariate Cox regression analysis and LASSO regression analysis showed that among these 20 genes,there were 4 genes associated with survival prognosis,i.e.,SRC,SLC2A1,PPARG,and RHOA,and a predictive model for the prognosis of hypoxia-driven HCC was successfully established based on these 4 genes.The Kaplan-Meier survival curve showed that the high prognostic risk group had a significantly lower overall survival rate than the low prognostic risk group in both the training set(HR=1.94,95％CI=1.36-2.76,P＜0.001)and the validation set(HR=2.13,95％CI=1.37-3.32,P＜0.001).The time-series ROC curve analysis showed that for the HCC patients in the training set,the model had an area under the ROC curve of 0.756,0.662,and 0.624,respectively,in predicting 1,3,and 5 year survival rates,while in the validation group,the model had an AUC of 0.583,0.607,and 0.751,respectively.The calibration curve analysis showed that the survival rate predicted by the model was basically consistent the actual survival rate in the validation set.RT-qPCR showed that the hypoxia group of HEPG2 and HUH7 cells had significantly higher mRNA expression levels of SRC,SLC2A1,PPARG,and RHOA than the blank group(t=4.52-12.47,P＜0.05).Conclusion This study successfully established a predictive model for the prognosis of HCC based on hypoxia-related genes,which has good predictive performance and calibration and holds considerable promise for further application in clini-cal practice.关键词
癌,肝细胞/缺氧/存活率/回归分析/预后/模型,统计学Key words
Carcinoma,hepatocellular/Hypoxia/Survival rate/Regression analysis/Prognosis/Models,statistical分类
医药卫生引用本文复制引用
LUO Yurong,HUANG Xijian,HUANG Ying,LIU Huan,CAI Jinzhen..基于缺氧相关基因的肝细胞癌预后预测模型的构建与验证[J].精准医学杂志,2025,40(6):499-504,6.基金项目
山东省自然科学基金项目(ZR2022MH292) （ZR2022MH292）
