Transcription factor BarH-like homeobox 2 ( BARX2 ), a member of the Bar-like homeobox gene family,involved in cell proliferation, differentiation, immune response and tumorigenesis. However, the potential role of BARX2 in the development of Liver hepatocellular carcinoma ( LIHC ) remains unclear. Therefore, we aimed to study the biological role of BARX2 in hepatocellular carcinoma.Through UALCAN, GTEx PORTAL, TIMER 2.0, LinkedOmics, SMART, MethSurv, Metascape, GSEA and STRING public databases, the BARX2 mRNA level, prognostic value, co-expressed genes, differentially expressed genes, DNA methylation and functional enrichment analysis of LIHC patients were studied. The relationship between BARX2 expression and various clinical or genetic parameters of LIHC patients was determined using data from the Cancer Genome Atlas ( TCGA ), Gene Expression Omnibus ( GEO ), and BEAT LIHC databases. In addition, the biological function of BARX2 in LIHC was studied in vitro. Through large-scale data mining, our study shows that BARX2 is differentially expressed in different normal and tumor tissues. BARX2 expression in LIHC tissues was significantly lower than in the corresponding controls, especially in patients with T2-4 stage. In patients with LIHC, overexpression of BARX2 is an independent poor prognostic factor associated with poor cytogenetic risk and gene mutations. Genomic hypermethylation of the BARX2 gene was associated with upregulated BARX2 expression and low OS in LIHC. Functional enrichment analysis showed that BARX2 has an immunomodulatory role and inflammatory response in LIHC occurrence.In conclusion, the oncogene BARX2 may serve as a new biomarker and prognostic factor for patients with LIHC. the immunomodulatory function of BARX2 deserves further validation in LIHC.

Objective Hepatocellular carcinoma (HCC) immunotherapy is a focus of current research. We established a model that can effectively predict the prognosis and efficacy of HCC immunotherapy by analyzing the immune genes of HCC. Methods Through the data mining of hepatocellular carcinoma in The Cancer Genome Atlas (TCGA), the immune genes with differences in tumor and normal tissues are screened, and then the univariate regression analysis is carried out to screen the immune genes with differences related to prognosis. The prognosis model of immune related genes is constructed by using the minimum absolute contraction and selection operator (lasso) Cox regression model in the TCGA training set data, The risk score of each sample was calculated, and the survival was compared with the Kaplan Meier curve and the receiver operating characteristic (ROC) curve to evaluate the predictive ability. Data sets from ICGC and TCGA were used to verify the reliability of signatures. The correlation between clinicopathological features, immune infiltration, immune escape and risk score was analyzed. Results Seven immune genes were finally determined as the prognostic model of liver cancer. According to these 7 genes, the samples were divided into the high and low risk groups, and the results suggested that the high-risk group had a poorer prognosis, lower risk of immune escape, and better immunotherapy effect. In addition, the expression of TP53 and MSI was positively correlated in the high-risk group. Consensus clustering was performed to identify two main molecular subtypes (named clusters 1 and 2) based on the signature. It was found that compared with cluster 1, better survival outcome was observed in cluster 2. Conclusion Signature construction and molecular subtype identification of immune-related genes could be used to predict the prognosis of HCC, which may provide a specific reference for the development of novel biomarkers for HCC immunotherapy.

Objective: Hepatocellular carcinoma (HCC) immunotherapy is a focus of current research. We established a model that can effectively predict the prognosis and efficacy of HCC immunotherapy by analyzing the immune genes of HCC.Methods: Through the data mining of hepatocellular carcinoma in The Cancer Genome Atlas (TCGA), the immune genes with differences in tumor and normal tissues are screened, and then the univariate regression analysis is carried out to screen the immune genes with differences related to prognosis. The prognosis model of immune related genes is constructed by using the minimum absolute contraction and selection operator (lasso) Cox regression model in the TCGA training set data, The risk score of each sample was calculated, and the survival was compared with the Kaplan Meier curve and the receiver operating characteristic (ROC) curve to evaluate the predictive ability. Data sets from ICGC and TCGA were used to verify the reliability of signatures. The correlation between clinicopathological features, immune infiltration, immune escape and risk score was analyzed.Results: Seven immune genes were finally determined as the prognostic model of liver cancer. According to these 7 genes, the samples were divided into the high and low risk groups, and the results suggested that the high-risk group had a poorer prognosis, lower risk of immune escape, and better immunotherapy effect. In addition, the expression of TP53 and MSI was positively correlated in the high-risk group. Consensus clustering was performed to identify two main molecular subtypes (named clusters 1 and 2) based on the signature. It was found that compared with cluster 1, better survival outcomes was observed in cluster 2.Conclusion: signature construction and molecular subtype identification of immune-related genes could be used to predict the prognosis of HCC, which may provide a specific reference for the development of novel biomarkers for HCC immunotherapy.