Construction Of Prognostic Risk Model With Immune Genes Of Hepatocellular Carcinoma Using Bioinformatics Analysis

Background and purpose: Background and Objective: Worldwide,hepatocellular carcinoma has become one of the most common malignant tumors.The number of cases of hepatocellular carcinom in China ranks the first in the world.Through the previous research that the occurrence and development of liver cancer factors are many,mainly including chronic hepatitis virus infection,long-term large amount of alcohol and eating aflatoxin contaminated food,etc.In China,however,chronic hepatitis B virus(HBV)infection dominates.In recent years,tumor immunity has received extensive attention,and immunotherapy has achieved good results in various cancer treatments.Currently,the treatment of advanced liver cancer is limited,and the liver is a special immune organ.Therefore,we hope to find a potential and effective immunotherapy model for liver cancer.Therefore,in order to further explore the immune genome of hepatocellular carcinom,we help to discover and improve the prognosis of hepatocellular carcinom patients with immune genes.TCGA project,on the other hand,provides a large number of expression spectrum data of HCC samples,which,combined with the matching clinical information,can be used to conduct a comprehensive study of HCC related immune genomics.Method: Based on the TCGA database,we downloaded and integrated RNA data from 263 HCC patients with complete clinical information and matched follow-up information.Genes differentially expressed in HCC were calculated and determined through the limma package,and IRG were screened for HCC with the IMMPORT online immune database.Subsequently,the biological functions of IRG of HCC were further explored through GO and KEGG functional enrichment analysis.Combined with clinical follow-up information,univariate COX regression analysis was used to screen out IRGs related to the prognosis of HCC.Moreover,TF,a transcriptional regulator closely related to the prognosis of HCC,was identified in combination with Cistrome Cancer,an online database.The potential regulatory mechanism between IRGs and TF of HCC was explored by means of biological analysis.The potential molecular properties of these HCC IRGs were further explored using an online database,cBioPortal.Multivariate COX regression analysis was used to construct a COX proportional hazard prediction model for immune-related genes associated with HCC prognosis.Patients were divided into high-risk group and low-risk group according to the high and low risk score,and therelationship between the number of tumor-infiltrating immune cells of 6 types and the high and low risk subgroup was analyzed and visualized by using the online TIMER database.The AUC values of the survivorROC curve were calculated using the survivorROC R software package,and the differences in clinical parameters were tested by independent T tests.The R language version used is R 3.6.0,and the drawing software is Cytoscap software 3.71.Results: 1988 differentially expressed genes were identified based on the RNA expression profile data of HCC.There were 203 differentially expressed IRGs.A univariate analysis identified 69 immunerelated genes that were significantly associated with clinical outcomes in HCC patients.These immune genes related to prognosis of liver cancer are mainly involved in the regulation of inflammatory response and humoral immune response.By constructing a COX proportional hazard model,the immune prognosis model associated with HCC prognosis was analyzed and determined,consisting of(S100A8,IL7 R,TNFRSF11B,JUND,CCL20,BIRC5,GHR,and SERPINA3).This prognostic model performs well in the prediction of HCC prognosis,and is related to pathological grade,tumor TMN stage,and clinical stage.In addition,prognostic indicators based on IRGs reflect infiltration of several types of immune cells.Conclusion: Our results screened out several key IRGs with clinical significance and constructed a risk prediction model of IRGs for HCC,which can effectively predict the prognosis of patients to a certain extent.It reveals the immune drivers and demonstrates the importance of IRG-based personalized immune characteristics in the recognition,monitoring and prognosis of HCC.