Development and Validation of a Novel 8 Immune Gene Prognostic Signature Based on the Immune Expression Profile for Hepatocellular Carcinoma

Background: The immune microenvironment plays a vital role in the development of hepatocellular carcinoma (HCC). This study explored novel immune-related biomarkers to predict the prognosis of patients with HCC. Methods: RNA-Seq data were downloaded from The Cancer Genome Atlas (TCGA). Univariate Cox regression was used to identify prognosis-related genes; the Lasso method was used to construct the prognosis risk model. Validation was performed on the International Cancer Genome Consortium (ICGC) cohort, and the C-index was calculated to evaluate its overall predictive performance. Western blots were conducted to evaluate the expression of genes. Results: There were 320 immune-related genes, 40 of which were significantly related to prognosis. Eight immune gene signatures (CKLF, IL12A, CCL20, PRELID1, CLAIN, ACVR2A, CD7, and FYN) were established by Lasso Cox regression analysis. This immune signature performed well in different cohorts and can be an independent risk factor for prognosis. In addition, the overall predictive performance of this model was higher than the other models reported previously. Conclusion: The predictive immune model will enable patients with HCC to be more accurately managed in immunotherapy.