Xu Dafeng, Wang Yu, Wu Jincai, Zhang Yuliang, Liu Zhehao, Chen Yonghai, Zheng Jinfang
Department of Hepatobiliary and Pancreatic Surgery, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China.
Geriatric Medicine Center, Hainan General Hospital, Hainan Affiliated Hospital of Hainan Medical University, Haikou, China.
Front Cell Dev Biol. 2021 Sep 23;9:686664. doi: 10.3389/fcell.2021.686664. eCollection 2021.
The prognosis of patients with hepatocellular carcinoma (HCC) is negatively affected by the lack of effective prognostic indicators. The change of tumor immune microenvironment promotes the development of HCC. This study explored new markers and predicted the prognosis of HCC patients by systematically analyzing immune characteristic genes. Immune-related genes were obtained, and the differentially expressed immune genes (DEIGs) between tumor and para-cancer samples were identified and analyzed using gene expression profiles from TCGA, HCCDB, and GEO databases. An immune prognosis model was also constructed to evaluate the 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). The expression and prognosis of core genes in pan-cancer cohorts were analyzed, and gene enrichment analysis was performed using clusterProfiler. Finally, the expression of the hub genes of the model was validated by clinical samples. Based on the analysis of 730 immune-related genes, we identified 64 common DEIGs. These genes were enriched in the tumor immunologic related signaling pathways. The first 15 genes were selected using RankAggreg analysis, and all the genes showed a consistent expression trend across multi-cohorts. Based on lasso cox regression analysis, a 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). Compared with other existing models, our model has better performance. CIBERSORT was used to assess the landscape maps of 22 types of immune cells in TCGA, GSE14520, and HCCDB18 cohorts, and found a consistent trend in the distribution of TIIC. In the high-risk score group, scores of Macrophages M1, Mast cell resting, and T cells CD8 were significantly lower than those of the low-risk score group. Different immune expression characteristics, lead to the different prognosis. Western blot demonstrated that ATG10, PRKCD, and SPP1 were highly expressed in cancer tissues, while IL18RAP and SLC11A1 expression in cancer tissues was lower. In addition, IL18RAP has a highly positive correlation with B cell, macrophage, Neutrophil, Dendritic cell, CD8 cell, and CD4 cell. The SPP1, PRKCD, and SLC11A1 genes have the strongest correlation with macrophages. The expression of ATG10, IL18RAP, PRKCD, SLC11A1, and SPP1 genes varies among different immune subtypes and between different T stages. The 5-immu-gene signature constructed in this study could be utilized as a new prognostic marker for patients with HCC.
缺乏有效的预后指标对肝细胞癌（HCC）患者的预后产生负面影响。肿瘤免疫微环境的改变促进了HCC的发展。本研究通过系统分析免疫特征基因，探索新的标志物并预测HCC患者的预后。获取免疫相关基因，利用来自TCGA、HCCDB和GEO数据库的基因表达谱，鉴定并分析肿瘤与癌旁样本之间的差异表达免疫基因（DEIGs）。还构建了免疫预后模型以评估不同队列中的预测性能。根据模型的风险评分将患者分为高、低两组，并使用不同算法评估肿瘤免疫浸润细胞（TIIC）。分析泛癌队列中核心基因的表达和预后，并使用clusterProfiler进行基因富集分析。最后，通过临床样本验证模型枢纽基因的表达。基于对730个免疫相关基因的分析，我们鉴定出64个常见的DEIGs。这些基因富集于肿瘤免疫相关信号通路。使用RankAggreg分析选择前15个基因，所有基因在多个队列中均呈现一致的表达趋势。基于套索cox回归分析，构建了一个5基因特征风险模型（ATG10、IL18RAP、PRKCD、SLC11A1和SPP1）。该特征具有很强的稳健性，能够稳定不同队列（TCGA-LIHC、HCCDB18和GSE14520）。与其他现有模型相比，我们的模型具有更好的性能。使用CIBERSORT评估TCGA、GSE14520和HCCDB18队列中22种免疫细胞的景观图，发现TIIC的分布存在一致趋势。在高风险评分组中，M1巨噬细胞、静息肥大细胞和CD8 T细胞的评分显著低于低风险评分组。不同的免疫表达特征导致不同的预后。蛋白质印迹法表明，ATG10、PRKCD和SPP1在癌组织中高表达，而IL18RAP和SLC11A1在癌组织中的表达较低。此外，IL18RAP与B细胞、巨噬细胞、中性粒细胞、树突状细胞、CD8细胞和CD4细胞高度正相关。SPP1、PRKCD和SLC11A1基因与巨噬细胞的相关性最强。ATG10、IL18RAP、PRKCD、SLC11A1和SPP1基因的表达在不同免疫亚型之间以及不同T分期之间存在差异。本研究构建的5免疫基因特征可作为HCC患者的一种新的预后标志物。
