Wang Zheng, Zhu Jie, Liu Yongjuan, Liu Changhong, Wang Wenqi, Chen Fengzhe, Ma Lixian
Department of Infectious Diseases, Qilu Hospital, Shandong University, Wenhua Xi Road 107, Jinan, 250012, Shandong, China.
Shandong Center for Disease Control and Prevention, Health Education Institute, Jinan, 250000, Shandong, China.
J Transl Med. 2020 Feb 11;18(1):67. doi: 10.1186/s12967-020-02255-6.
Growing evidence has suggested that immune-related genes play crucial roles in the development and progression of hepatocellular carcinoma (HCC). Nevertheless, the utility of immune-related genes for evaluating the prognosis of HCC patients are still lacking. The study aimed to explore gene signatures and prognostic values of immune-related genes in HCC.
We comprehensively integrated gene expression data acquired from 374 HCC and 50 normal tissues in The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) analysis and univariate Cox regression analysis were performed to identify DEGs that related to overall survival. An immune prognostic model was constructed using the Lasso and multivariate Cox regression analyses. Furthermore, Cox regression analysis was applied to identify independent prognostic factors in HCC. The correlation analysis between immune-related signature and immune cells infiltration were also investigated. Finally, the signature was validated in an external independent dataset.
A total of 329 differentially expressed immune-related genes were detected. 64 immune-related genes were identified to be markedly related to overall survival in HCC patients using univariate Cox regression analysis. Then we established a TF-mediated network for exploring the regulatory mechanisms of these genes. Lasso and multivariate Cox regression analyses were applied to construct the immune-based prognostic model, which consisted of nine immune-related genes. Further analysis indicated that this immune-related prognostic model could be an independent prognostic indicator after adjusting to other clinical factors. The relationships between the risk score model and immune cell infiltration suggested that the nine-gene signature could reflect the status of tumor immune microenvironment. The prognostic value of this nine-gene prognostic model was further successfully validated in an independent database.
Together, our study screened potential prognostic immune-related genes and established a novel immune-based prognostic model of HCC, which not only provides new potential prognostic biomarkers and therapeutic targets, but also deepens our understanding of tumor immune microenvironment status and lays a theoretical foundation for immunotherapy.
越来越多的证据表明，免疫相关基因在肝细胞癌（HCC）的发生和发展中起关键作用。然而，免疫相关基因在评估HCC患者预后方面的实用性仍不足。本研究旨在探索HCC中免疫相关基因的基因特征及预后价值。
我们全面整合了从癌症基因组图谱（TCGA）中获取的374例HCC组织和50例正常组织的基因表达数据。进行差异表达基因（DEG）分析和单因素Cox回归分析，以鉴定与总生存期相关的DEG。使用Lasso和多因素Cox回归分析构建免疫预后模型。此外，应用Cox回归分析确定HCC的独立预后因素。还研究了免疫相关特征与免疫细胞浸润之间的相关性分析。最后，在外部独立数据集中验证该特征。
共检测到329个差异表达的免疫相关基因。通过单因素Cox回归分析，确定64个免疫相关基因与HCC患者的总生存期显著相关。然后我们建立了一个TF介导的网络来探索这些基因的调控机制。应用Lasso和多因素Cox回归分析构建基于免疫的预后模型，该模型由9个免疫相关基因组成。进一步分析表明，在调整其他临床因素后，该免疫相关预后模型可能是一个独立的预后指标。风险评分模型与免疫细胞浸润之间的关系表明，九基因特征可以反映肿瘤免疫微环境的状态。该九基因预后模型的预后价值在一个独立数据库中得到了进一步成功验证。
总之，我们的研究筛选出了潜在的预后免疫相关基因，并建立了一种新的基于免疫的HCC预后模型，这不仅提供了新的潜在预后生物标志物和治疗靶点，还加深了我们对肿瘤免疫微环境状态的理解，为免疫治疗奠定了理论基础。
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