Background and Aims Hepatocellular carcinoma (HCC) is the most common type of liver cancer. The prognosis of HCC patients is poor, and its effective prognosis prediction is also facing significant challenges. Several studies have shown that the genetic markers associated with the E2F gene family and immune microenvironment are important prognostic factors for cancers. Therefore, this study was conducted to screen the HCC gene signatures related to the E2F gene family and immune microenvironment using the TCGA database, establish a new risk assessment model for HCC and predict the potential therapeutic targets for HCC.Methods A large HCC (LIHC) dataset (n=424) from the TCGA database was downloaded. Gene set enrichment analysis, single sample gene set enrichment analysis, and differential gene expression analysis was performed, marker genes were screened and modeled by Lasso regression, patient scores were calculated according to the model, and patients were divided into high-risk and low-risk groups. Multiple statistical methods, such as the receiver operating characteristic (ROC) curve, Kaplan-Meier survival curve, and Cox regression analysis, were used to verify the model's reliability. R language software was used for all statistical analyses. Finally, genetic alterations of the marker genes from the risk model were queried in the TCGA-HCC samples in the Cbioportal database. The protein interaction information was downloaded from the String database and visualized in Cytoscape software.Results After identification of the E2F target genome and immune-related differential genes which were closely related to HCC, seven genes (CYR61, fbln5, LPA, SAA1, SDC3, serpine1, ssrp1) significantly associated with the overall survival rate of HCC patients were screened, and a prognostic 7-mRNA signature model was established: risk score=-0.55×CYR61 expression-0.18×FBLN5 expression-0.17×LPA expression -0.06×SAA1 expression +0.31×SDC3 expression+0.38 ×SERPINE1 expression+1.08×SSRP1 expression The ROC AUC value of the model was 0.846. Kaplan-Meier survival curve showed that patients with high-risk scores had a poor prognosis (P<0.001). The degree of discrimination for prognosis of high and low-risk scores was similar to those of tumor size and UICC stage and higher than those of lymph node metastasis, distant metastasis, and BMI. Multivariate Cox regression analysis showed that the predictive ability of the 7-mRNA signature model was independent of clinical factors. In addition, the key genes SERPINE1 and LPA in the 7 genes were found by combining proteomics, which predicted that inhibiting plasminogen activation was probably a new target approach for treating HCC.Conclusion This study reveals the correlation between seven genes and E2F targets and immunity, provides new biomarkers for poor prognosis of HCC patients and establishes a prognostic risk score model with high predictive accuracy. However, the predictive ability of the polygenic prognosis model still needs to be confirmed by many evidence-based medical practices from multiple centers, and the gene function and participation mechanism of the included polygenic models still need to be further studied.