Construction And Verification Of The Prognostic Gene Model For Hepatocellular Carcinoma Based On Necroptosis-related Genes

Background and Objectives:Hepatocellular carcinoma(HCC)has the characteristics of complex etiology and strong heterogeneity.Many patients are in the late stage of poor prognosis when they are diagnosed.There is an urgent need for more accurate prognostic risk assessment methods and new diagnostic biomarkers and molecular therapeutic targets.Necroptosis is a new type of programmed cell death that is closely related to the occurrence and development of tumors.At present,the relationship between necroptosis-related genes(NRGs)and the prognosis of patients with HCC is not clear.The purpose of this study is to screen the NRGs related to the prognosis of HCC patients and construct a prognostic model on this basis,and to evaluate and verify the prognostic value of the prognostic model,so as to provide help for risk stratification and individualized diagnosis and treatment of HCC patients.Methods:The clinical information and sequencing data of normal samples and HCC samples were collected from The Cancer Genome Atlas(TCGA)database for this study,and the differentially expressed NRGs were screened out.The protein interaction network was constructed according to the differentially expressed genes,the consensus clustering analysis of HCC patients was carried out,and then univariate Cox regression analysis was used to screen the NRGs related to the survival time of HCC patients.The optimal penalty parameters were obtained by the least absolute shrinkage and selection operator(LASSO)Cox regression analysis,and the prognostic model was established.According to the expression level and risk coefficient of genes in the prognostic model,the prognostic risk score of HCC patients was obtained.Principal component analysis(PCA),t-distributed Stochastic Neighbor Embedding(t-SNE)analysis,Kaplan-Meier survival curve,and receiver operating characteristic(ROC)area under the curve(AUC)were used to evaluate the prediction efficiency of the risk score model.Univariate and multivariate Cox regression analysis was used to determine the independent prognostic factors of patients with HCC.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Gnomes(KEGG)enrichment analyses were used to identify the cellular functions and biological processes involved in differential genes.Single sample Gene Set Enrichment Analysis(ssGSEA)is used to explore the relevance of genes to immune-related functions and immune cells.Hepatocellular carcinoma samples from the International Cancer Genome Consortium(ICGC)database are used as a validation set for external validation of the prognostic model.Results:In this study,a total of 49 differentially expressed NRGs were screened from normal samples and HCC samples,and protein interaction networks showed that there was a close relationship between differentially expressed genes.Through the differential expression analysis of the two subgroups obtained by the consensus clustering analysis,28 differentially expressed NRGs were obtained.Univariate Cox regression analysis identified 18 genes related to the survival time of HCC patients from 28 differentially expressed NRGs.By performing LASSO Cox regression analysis to narrow the range of candidate genes,a prognostic model including 10 key prognostic genes(CDKN2A,HAT1,HSP90AA1,IPMK,KLF9,MYCN,NDRG2,PLK1,SQSTM1,TNFRSF21)was constructed,most of which were related to poor prognosis in patients with HCC.According to the median risk score of the prognostic model in the TCGA cohort,patients with HCC were classified into high-risk and low-risk groups.The ROC curves showed good sensitivity and specificity of the model,and univariate and multivariate Cox regression analyses confirmed that the risk score was an independent factor for the prognosis of HCC patients.The ICGC cohort as an external cohort validates the predictive efficiency of the prognostic gene model and confirms that the model has a good prognostic value.The results of the enrichment analysis showed that differentially expressed genes in the high-risk and low-risk groups were associated with fatty acid metabolism,retinol metabolism,drug metabolism,and exogenous metabolic processes.Conclusion:The prognostic model constructed in this study can be used as a supplement to the existing risk assessment system to make a more accurate risk assessment and formulate individual diagnosis and treatment measures for HCC patients,so as to optimize the treatment effect and improve the prognosis of patients.In addition,the differentially expressed genes and prognosis-related genes screened in this study can provide help for the discovery of new diagnostic biomarkers and molecular therapeutic targets.