Du Suming, Xu Jinhui, Shen Jiajia, Zhang Xiaojin, Hu Huanzhang, Huang Xinghua
Department of Hepatobiliary Surgery, The 900th Hospital of the Joint Logistic Support Force of People's Liberation Army, Fuzhou 350025, China.
Gastroenterol Res Pract. 2022 Jun 28;2022:2746156. doi: 10.1155/2022/2746156. eCollection 2022.
For those patients with hepatocellular carcinoma (HCC), it is really a heavy burden. Herein, the immune genes of HCC were analyzed in groups to determine prognostic biomarkers related to immune genes in HCC. The mRNA data, clinical data in TCGA-LIHC dataset, and immune gene in the ImmPort database were collected for the combining usage with -means concordance clustering to cluster HCC patients according to the immune gene matrix. Based on ssGSEA analysis result, HCC patients were sorted into high- and low-immune subtypes, and survival curve presented that patients in high-immune subtypes had a better prognosis. Subsequently, differential expression analysis was performed to obtain immune-related differentially expressed genes (IRGs). Cox and lasso analyses were performed for obtaining five optimal immune genes related to prognosis, and a risk assessment model was then established. Patient samples in the training and validation sets were, respectively, divided into high- and low-risk groups. - survival curves presented a better prognosis of patients in the low-risk group than in the high-risk group. The ROC curve indicated that this model was finely used for the prediction of prognosis. In addition, immune infiltration assessment revealed that NR0B1 and FGF9 had potential to impact the tumor immune microenvironment. Finally, using qRT-PCR and transwell assays, it was demonstrated that the macrophage chemotaxis was enhanced when NR0B1 and FGF9 were highly expressed in HCC cells. In general, a 5-gene prognostic risk assessment model was constructed based on immune genes and bioinformatics analysis methods, which provides some reference for the prognosis of HCC as well as immunotherapy.
对于那些肝细胞癌（HCC）患者来说，这确实是一个沉重的负担。在此，对HCC的免疫基因进行分组分析，以确定与HCC免疫基因相关的预后生物标志物。收集了TCGA-LIHC数据集中的mRNA数据、临床数据以及ImmPort数据库中的免疫基因，以便与K均值一致性聚类结合使用，根据免疫基因矩阵对HCC患者进行聚类。基于单样本基因集富集分析（ssGSEA）结果，将HCC患者分为高免疫亚型和低免疫亚型，生存曲线显示高免疫亚型患者预后较好。随后，进行差异表达分析以获得免疫相关差异表达基因（IRGs）。进行Cox和套索分析以获得与预后相关的五个最佳免疫基因，然后建立风险评估模型。训练集和验证集中的患者样本分别分为高风险组和低风险组。生存曲线显示低风险组患者的预后优于高风险组。ROC曲线表明该模型可很好地用于预后预测。此外，免疫浸润评估显示NR0B1和FGF9有可能影响肿瘤免疫微环境。最后，通过qRT-PCR和Transwell实验证明，当NR0B1和FGF9在HCC细胞中高表达时，巨噬细胞趋化性增强。总体而言，基于免疫基因和生物信息学分析方法构建了一个5基因预后风险评估模型，为HCC的预后以及免疫治疗提供了一些参考。
