Systematic Characterization of Novel Immune Gene Signatures Predicts Prognostic Factors in Hepatocellular Carcinoma - Xu, Dafeng; Wang, Yu; Wu, Jincai; Zhang, Yuliang; Liu, Zhehao; Chen, Yonghai; Zheng, Jinfang (Frontiers in Cell and Developmental Biology, 2021)
Background: The prognosis of patients with hepatocellular carcinoma (HCC) is negatively affected by the lack of effective prognostic indicators. The change of immune microenvironment contributes to the development of HCC. This study explored new markers and predicted the prognosis of HCC patients by systematically analyzing immune characteristic genes. Methods: Immune-related genes were obtained, and the differentially expressed immune genes (DEIGs) between tumor and para-cancer samples were identified and analyzed by using gene expression profile from TCGA, HCCDB, and GEO databases. An immune prognosis model was also constructed to evaluate predictive performance in different cohorts. The high and low groups were divided based on the risk score of the model, and different algorithms were used to evaluate the tumor immune infiltration cell (TIIC). Finally, the expression of the hub genes of the model was validated by clinical samples. Results: Based on the analysis of 730 immune-related genes, we identified 64 common DEIGs. These genes are closely related to the tumor immunologic pathways of NF−kappa B signaling pathway, TNF signaling pathway, and Cytokine−cytokine receptor interaction. The first 15 genes were selected using RankAggreg analysis, and all the genes exhibited a consistent expression trend across multi-cohorts. Based on lasso cox regression analysis, 5-gene signature risk model (ATG10, IL18RAP, PRKCD, SLC11A1 and SPP1) was constructed. The signature has strong robustness and can stabilize different cohorts (TCGA-LIHC, HCCDB18, and GSE14520). Our model performs better in comparison to other models. CIBERSORT was used to assess the landscape maps of 22 types of immune cells in TCGA, GSE14520, and HCCDB18 cohorts, and revealed a consistent trend in the distribution of TIIC. The high-risk score group’s scores of Macrophages M1, Mast cell resting, and T cells CD8 were lower than those of the low-risk score group. To some extent, different immune expression characteristics lead to the different prognosis. Western blot demonstrated that ATG10, PRKCD, SLC11A1, and SPP1 were highly expressed in cancer tissues, while IL18RAP’s expression in cancer tissues was lower. Conclusion: The 5-immu-gene signature constructed in this study could be utilized as a new prognostic marker for patients with HCC.