Development Of An Immune-related Signature Predicting Prognosis Of Patients In Hepatocellular Carcinoma

Background:Hepatocellular carcinoma(HCC)causes a significant proportion of tumor-related mortality,threatening thousands of lives worldwide.Given poor prognosis and the lack of efficient therapy for advanced HCC,immunotherapy has emerged as an increasingly important role.Although some progress has been made in the immunotherapy of HCC,the treatment effect is not satisfactory.And,there has been little researches about the immune related genes in HCC.Therefore,the analysis of immune-related gene profiling of HCC is very important,which can provide guidance for immunotherapy.Purposes:There are two main purposes of this project,(1)To construct the immune-related signature that can be used as predictor of HCC patients,using immune-related differential genes,and to evaluate predictive power of this signature.(2)To analyze the relationships between the immune-related signature and clinical characteristics,and to explore the immune cells infiltrations and potential regulatory mechanisms of the immune-related signature.Methods:(1)Get the hepatocellular carcinoma expression profile data and clinical data through the TCGA database and the immune-related genes through the ImmPort database,and use the "limma" package of Bioconductor in R language to screen out differentially expressed immune-related genes.(2)Univariate and multivariate Cox analysis based on differentially expressed immune-related genes are conducted and immune-related genes serving as independent prognostic predictors for HCC patients are chosen,and coxph()of the "survival"package in R is used to build the optimal immune-related signature based on those chosen genes.(3)HCC patients are divided into high-risk group and low-risk group according to risk score and the prognostic ability of the signature is evaluated by KM analysis and ROC curve.(4)The "survival" package of R is used to analyze the relationship between the immune-related signature and clinical characteristics to evaluate whether the signature is an independent prognostic factor for HCC patients.(5)Obtain HCC tumor immune cell infiltration data by TIMER database and analyze the relationships between immune-related signature and immune cells(B cells,CD4 T cells,CD8+T cells,macrophages,neutrophils,Dendritic cells)using R.(6)Regulatory networks related to genes in the signature and transcription factors is constructed through R and Cytoscape software.(7)Protein-protein interaction network,GO and KEGG function enrichment analysis for immune-related genes that can predict HCC prognosis are formed through the STRING online database and DAVID website,respectively.Results:(1)The expression profile data and related clinical information of 374 hepatocellular carcinoma patients and 50 adjacent control patients were obtained through the TCGA database and 1181 immune-related genes were obtained through the ImmPort database.Then 116 immune-related genes were selected through differential expression analysis(96 up-regulated genes,20 down-regulated genes).(2)Nine immune-related genes that can independently predict the prognosis of hepatocellular carcinoma were screened through univariate and multivariate Cox analysis.These nine genes were BIRC5,DKK1,PTHLH,FGF13,SPP1,IL11,IL17D,FOS,and PLXNA33.(3)Through the coxph()function of the "survival" package of R language,we constructed a seven-gene prognostic risk prediction gene model(Risk score=BIRC5*0.0238+FOS*0.0055+DKK1*0.0085+FGF13*0.3432+IL11*0.0135+IL17D*0.0878+SPP1*0.0003).(4)We further explored the relationship between the immune-related signature and clinical traits,and the results showed that patients with viral hepatitis B infection were significantly associated with high risk score,with a correlation coefficient of 2.096.(5)The risk score calculated by the immune-related signature was significantly related to the infiltration of B cells,CD8 T+cells,macrophages,neutrophils,and dendritic cells(p<0.05),with correlation coefficients of 0.117,0.277,and 0.282,respectively.0.188 and 0.238,respectively.(6)In addition,we have also constructed a regulatory network for genes in the signature and transcription factors.(7)A protein-protein interaction network for HCC prognosis-related immune-related genes was constructed,and the related pathways in which these genes are enriched were showed.Conclusion:Using the data in the Cancer Genome Atlas(TCGA)database,we constructed a seven-gene immune-related risk prediction model with the ability to predict the prognosis of HCC patients.At the same time,we evaluated the correlation between the immune-related signature and clinical characteristics and the results confirmed that the risk score is closely related to hepatitis B virus infection.The relationship between the immune-related signature and immune cell infiltration and the potential regulatory mechanisms were also explored through bioinformatics analysis,which had guiding significance for immunotherapy of HCC.