Establishment Of Pre-immune Gene Risk Prediction Model For Hepatitis B-associated Hepatocellular Carcinoma Based On Bioinformatics

Objective: To search for immune genes(IRG)related to the occurrence and development of hepatitis B(HBV)-related liver cancer through bioinformatics,and establish a risk prediction model.It provides a model reference for the subsequent risk prediction of HBV-related liver cancer patients and provides relevant markers for clinical prognostic immunization research.Methods:(1)Download the RNA-seq data of HCC samples from the TCGA data portal(https://cancergenome.nih.gov/),and standardize the raw sequencing data to obtain genomic gene expression for analysis data.The gene expression data of HBV-related liver cancer samples and adjacent tissue samples and clinical data of patients with tissue samples were screened.(2)Export immune-related genes(IRGs)through the immunology database and the portal analysis website(innate DB)database.The HBV-related liver cancer IRGs were screened from the gene expression data of HBV-related liver cancer in TCGA.Then use R "edge R" and "limma" software packages for differential expression analysis to screen for differentially expressed IRGs between HBV-related liver cancer tissue samples and adjacent tissue samples,and set the false discovery rate(FDR)<0.05 and fold-change(multiplier Change)>1 as the threshold.(3)Use Web online tool "DAVID" to perform GO function annotation and KEGG pathway enrichment analysis to explore the biological p ROCesses and signaling pathways that differentially expressed IRGs may participate in.(4)Use the R software "survival" package for single factor COX proportional risk regression analysis,and then incorporate COX single factor statistically significant differences IRGs into the multi-factor COX proportional risk regression model,and use the multi-factor COX regression coefficient to establish a risk prediction model And prognostic risk score(Risk Score).(5)Evaluation and verification of the effect of the prognostic model: Kaplan-Meier survival analysis and ROC curve(Receiver operating characteristic curve,ROC curve)of the prediction model are drawn,and the area of ??the curve under the ROC(AUC)value and the cutoff value are obtained to test the accuracy of the prediction of the built prognostic risk model.Then the patients were divided into high-risk group and low-risk group according to the cutoff value.Kaplan-Meier survival analysis was used to test the clinical prognostic value of the multigene prognosis model in HBV-related liver cancer.(6)The TCIA database was used to study the degree of immune cell infiltration in HBV-related liver cancer samples and the genomic sequence of the modeled genes.(7)Use the GEO database as a verification set to verify the established risk prediction model.Results:(1)95 HBV-related liver cancer tissue samples were screene d through the TCGA database,and 50 were adjacent to the cancer.Each sample has 19754 transcriptome data.(2)The intersection of 19,754 gene s and IGRs in the innate DB database yielded 3,876 genes related to the i mmunity-free function.(3)Using "edge R" and "limma" in R language to analyze the immune gene expression data of HBV-related liver cancer,516 differential IRGs were obtained,including 315 up-regulated genes and 201 down-regulated genes.(4)The results of GO enrichment analysis indic ate that "redox p ROCess","cell matrix exosomes",and "heterodimerizatio n activity of proteins" are the most significant biological items in biologi cal p ROCesses,cellular components,and molecular functions,respectively.KEGG enrichment analysis results show that differentially expressed IRG s are enriched in the "metabolic pathway" signaling pathway.(5)After si ngle factor COX regression analysis,49 differential IRGs were selected,a nd then the multifactor COX proportional hazard regression model selecte d 6 prognostic related IRGs,and established a prognostic immune gene ri sk prediction model.Risk Score=(RPL23A*-0.00916)+(SERTAD2*0.495443)+(MTRF1*-1.10422)+(NUCB2*-0.09583)+(RAD23A*-0.05712)+(TLL2*9.564602).(6)According to the cuttoff value in the risk prediction model esta blished by these 6 genes,it can be seen that the risk model samples of HBV-related liver cancer high-risk patients have significantly lower OS an d lower risk patients(p=0.0056),which was obtained in independent verif ication samples.The same result(p=0.0033).The AUC of the risk predict ion model in the TCGA data=0.692,and the AUC=0.635 in the model in dependently verified samples,indicating that the prediction model has goo d prediction ability.(8)The TCIA database shows that the genetic change s of the genomic sequence of the six genes in the risk prediction model obtained in this study are mainly concentrated on the amplification and d eep deletion of DNA fragments.Macrophage infiltration is the largest in i mmune cell infiltration.Conclusion: Based on the high-throughput expression profile data of HBV-related liver cancer patients in different public databases,this study established a HBV-related liver cancer risk prediction model containing 6prognostic-related IRGs.This risk model provides a candidate marker for clinical individualized treatment and follow-up by predicting the prognosis of HBV-related liver cancer patients.It also provides a certain theoretical basis for the research on the immune mechanism of HBV-related liver cancer in the future.