Construction Of Models For Prognosis,recurrence And Diagnosis Of Hepatocellular Carcinoma Using Copy Number Variation-and DNA Methylation-driven Genes

Aim:The occurrence of hepatocellular carcinoma(HCC)includes a series of genetic and epigenetic changes.We conducted a comprehensive analysis of gene expression data,copy number variation(CNV)data,and DNA methylation data of liver cancer,aiming to establish prognosis,recurrence and diagnosis models for HCC.We hope the results could be translated into clinical practice.Methods:We performed a comprehensive analysis of the gene expression data,CNV data and DNA methylation data of liver cancer to identify CNV-driven genes and DNA methylation-driven genes in liver cancer.The clinical information of patients was collected,and these genes were subjected to univariate Cox regression analysis,LASSO(least absolute shrinkage and selection operator)regression analysis,and multivariate Cox regression analysis to establish a prognostic model.At the same time,we also used DNA methylation driver genes to construct a recurrence and diagnosis model of liver cancer,respectively.All models have been externally verified.Results:After integrative analysis of CNVs and corresponding mRNA expression profiles,568 CNV-driven genes were identified.Sixty-three CNV-driven genes were found to be markedly associated with overall survival(OS)after univariate Cox regression analysis.After LASSO and multivariate Cox regression analysis,eight CNV-driven genes were screened to generate a prognostic risk model.Compared with low-risk group,the OS of patients in the high-risk group was significantly shorter in both the TCGA[hazard ratio(HR)=6.14,95%confidence interval(CI):2.72-13.86,P<0.001]and ICGC(HR=3.23,95%CI:1.1 7-8.92,P<0.001)datasets.Further analysis revealed the infiltrating neutrophils were positively correlated with risk score.Meanwhile,the high-risk group was associated with higher expression of immune checkpoint genes.After integrative analysis of DNA methylation and corresponding gene expression profile,a total of 123 DNA methylation-driven genes were identified.Two of these genes(SPP1 and LCAT)were chosen to construct the prognostic model.The high-risk group showed a markedly unfavorable prognosis compared to the low-risk group in both training(HR=2.81;P<0.001)and validation(HR=3.06;P<0.001)datasets.Multivariate Cox regression analysis indicated the prognostic model to be an independent predictor of prognosis(P<0.05).Also,the recurrence model successfully distinguished the HCC recurrence rate between the high-risk and low-risk groups in both training(HR=2.22;P<0.001)and validation(HR=2;P<0.01)datasets.The two diagnostic models provided high accuracy for distinguishing HCC from normal samples and dysplastic nodules in the training and validation datasets,respectively.Conclusions:We identified CNV and DNA methylation-driven genes for HCC,and further constructed and validated prognostic,recurrence,and diagnostic models based on these driver genes.Th e results obtained by integrating multidimensional genomic data offer novel research directions for HCC biomarkers and new possibilities for individualized treatment of patients with HCC.