Construction Of Prognostic Model Based On Chemokine-related Genes In Hepatocellular Carcinoma

Objective:Using bioinformatics analysis of the relationship between the differential expressed chemokine-related genes and the prognosis of hepatocellular carcinoma(hepatocellular carcinoma,HCC),we aimed to identify HCC prognosis biomarkers,to establish a HCC prognosis evaluation model of chemokine-related genes,and verified its predictive value for HCC,and further analyzed the application value of the model for clinical HCC patients.Methods:We collected 1,675 chemokine-related genes from the gene and genecards databases.Download transcriptome data and clinicopathological information data of hepatocellular carcinoma in the Cancer Genome Atlas.Extract chemokine-related genes expression data in the HCC transcriptome data.Extract chemokine-related differentially expressed genes in cancerous and paracancerous tissues and make a functional enrichment analysis.Screen Chemokine-associated genes with prognostic value using univariate Cox regression analysis.Construct a HCC prognosis model based on chemokine-related genes by PPI analysis,random forest algorithm and multivariate Cox regression analysis.Calculate the risk score by predict function and divide the HCC patients in TCGA database into high risk and low risk groups.Then,drew the Kaplan-Meier survival curve and analyze the relationship between risk score and overall survival of patients.Draw receiver operating characteristic curve and calculate the area under the ROC curve to evaluate the prognostic model performance.A paired t-test using R language was used to compare the differential expression of prognostic genes between HCC and paraneoplastic tissues in TCGA.Survival analysis of prognostic genes were made by GEPIA2 databases.External validation were made using the ICGC database.Multivariate Cox regression analysis were used to evaluate whether the model had independent predictive value for prognosis in HCC patients.Results:In total,142 chemokine-related and differentially expressed genes were identified in our study.KEGG enrichment analysis showed that differentially expressed genes were mainly enriched in cytokine receptor signaling pathway,chemokine signaling pathway,viral protein receptor signaling pathway,NOD class receptor signaling pathway and other pathways.GO enrichment analysis found that differentially expressed genes are mainly focused on cytokine production and binding to the corresponding receptors,leukocyte wandering,cell chemotaxis,immunomodulation,DNA transcription,extracellular matrix,secretory granules,etc.Univariate Cox regression analysis revealed that 35chemokine-associated differentially expressed genes were associated with overall survival(both P <0.05).Ten important genes were screened out using PPI and random forest methods in these 35 chemokine-related genes.Finally,a hepatocellular carcinoma prognosis model containing four genes was constructed by multivariate Cox regression analysis.The area under the ROC curve of both this model and the external validation group was greater than 0.6.The phenomenon showed that the model has a good predictive ability.A multivariate Cox regression analysis of clinical information and risk score demonstrated that the four-gene risk prognostic score is an independent factor influencing the outcome of patients with hepatocellular carcinoma(P <0.05).Conclusion:This study successfully constructed a HCC prognosis model based on chemokine-related genes and validated that the model could help to assess the prognosis of HCC patients and could serve as an independent prognostic marker for HCC patients.