Liver Disease Center, The Affiliated Hospital of Qingdao University, No. 59 Haier Road, Qingdao, 266003, China.
BMC Cancer. 2022 May 5;22(1):496. doi: 10.1186/s12885-022-09587-0.
Hepatocellular carcinoma (HCC) has a high degree of malignancy and a poor prognosis. Immune infiltration-related genes have shown good predictive value in the prognosis of many solid tumours. In this study, we established and verified prognostic biomarkers consisting of immune infiltration-related genes in HCC. Gene expression data and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. Differential gene expression analysis, univariate Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression algorithm were used to screen prognostic immune infiltration-related genes and to construct a risk scoring model. Kaplan-Meier (KM) survival plots and receiver operating characteristic (ROC) curve analysis were used to evaluate the prognostic performance of the risk scoring model in the TCGA-HCC cohort. In addition, a nomogram model with a risk score was established, and its predictive performance was verified by ROC analysis and calibration plot analysis in the TCGA-HCC cohort. Gene set enrichment analysis (GSEA) identified pathways and biological processes that may be enriched in the high-risk group. Finally, immune infiltration analysis was used to explore the characteristics of the tumour microenvironment related to the risk score. We identified 17 immune infiltration-related genes with prognostic value and constructed a risk scoring model. ROC analysis showed that the risk scoring model can accurately predict the 1-year, 3-year, and 5-year overall survival (OS) of HCC patients in the TCGA-HCC cohort. KM analysis showed that the OS of the high-risk group was significantly lower than that of the low-risk group (P < 0.001). The nomogram model effectively predicted the OS of HCC patients in the TCGA-HCC cohort. GSEA indicated that the immune infiltration-related genes may be involved in biological processes such as amino acid and lipid metabolism, matrisome and small molecule transportation, immune system regulation, and hepatitis virus infection. Immune infiltration analysis showed that the level of immune cell infiltration in the high-risk group was low, and the risk score was negatively correlated with infiltrating immune cells. Our prognostic model based on immune infiltration-related genes in HCC could help the prognostic assessment of HCC patients and provide potential targets for HCC inhibition.
肝细胞癌 (HCC) 恶性程度高，预后差。免疫浸润相关基因在许多实体瘤的预后预测中表现出良好的预测价值。本研究旨在建立和验证 HCC 中由免疫浸润相关基因组成的预后生物标志物。从癌症基因组图谱 (TCGA) 数据库中下载基因表达数据和临床数据。采用差异基因表达分析、单因素 Cox 回归分析和最小绝对收缩和选择算子 (LASSO) 回归算法筛选与免疫浸润相关的预后基因，并构建风险评分模型。Kaplan-Meier（KM）生存图和受试者工作特征（ROC）曲线分析用于评估风险评分模型在 TCGA-HCC 队列中的预后性能。此外，还建立了一个带有风险评分的列线图模型，并通过 TCGA-HCC 队列的 ROC 分析和校准图分析验证了其预测性能。基因集富集分析（GSEA）鉴定了可能在高危组中富集的通路和生物学过程。最后，进行免疫浸润分析以探讨与风险评分相关的肿瘤微环境特征。我们确定了 17 个具有预后价值的免疫浸润相关基因，并构建了风险评分模型。ROC 分析表明，该风险评分模型能够准确预测 TCGA-HCC 队列中 HCC 患者的 1 年、3 年和 5 年总生存率（OS）。KM 分析表明，高危组的 OS 明显低于低危组（P<0.001）。列线图模型有效地预测了 TCGA-HCC 队列中 HCC 患者的 OS。GSEA 表明，免疫浸润相关基因可能参与了氨基酸和脂质代谢、基质和小分子转运、免疫系统调节和肝炎病毒感染等生物学过程。免疫浸润分析表明，高危组中免疫细胞浸润水平较低，且风险评分与浸润免疫细胞呈负相关。我们基于 HCC 中免疫浸润相关基因的预后模型有助于 HCC 患者的预后评估，并为 HCC 抑制提供潜在靶点。
