Pan Banglun, Liu Lin, Li Wei
Key Laboratory of Laboratory Medicine, Ministry of Education of China, School of Laboratory Medicine and Life Sciences Wenzhou Medical University Wenzhou China.
Zhejiang Provincial Key Laboratory of Medical Genetics Wenzhou Medical University Wenzhou China.
Health Sci Rep. 2020 Nov 10;3(4):e202. doi: 10.1002/hsr2.202. eCollection 2020 Dec.
Hepatocellular carcinoma (HCC) is one of the most common heterogeneous tumors that occurs after chronic liver diseases and hepatitis virus infection. Immune-related genes (IRGs) and their ligands regulate the homeostasis of tumor microenvironment, which is essential for the treatment of HCC and its prognosis. This study aimed to investigate the clinical value of IRGs in predicting the prognosis of HCC.
We downloaded RNA-seq data and clinical information from TCGA database. Samples were randomly divided into training cohort and testing cohort. The "limma" R package was performed to identify differentially expressed IRGs (DEIRGs) between HCC group and normal group. Prognostic DEIRGs (PDEIRGs) were obtained by univariate Cox analysis. LASSO and multivariate Cox analysis were used, and a prognostic risk model was constructed. In order to better demonstrate the clinical value of our model in predicting overall survival rate, a nomogram was constructed. To further investigate the molecular mechanism of our model, gene set enrichment analysis (GSEA) was performed.
Compared with the low-risk group, the high-risk group had a significantly worse prognosis. Moreover, our prognostic risk model can accurately stratify tumor grade and TNM stage. Importantly, in our model, not only immune checkpoint genes were well predicted, but also human leucocyte antigen-I molecules were revealed. GSEA suggested that "MAPK signaling pathway," "mTOR signaling pathway," "NOD like receptor signaling pathway," "Toll like receptor signaling pathway," "VEGF signaling pathway," "WNT signaling pathway" had significant correlations with the high-risk group.
Overall, our study showed that our prognostic risk model can be used to assess prognosis of HCC, which may provide a certain basis for the survival rate of patients with HCC.
肝细胞癌（HCC）是慢性肝病和肝炎病毒感染后最常见的异质性肿瘤之一。免疫相关基因（IRGs）及其配体调节肿瘤微环境的稳态，这对HCC的治疗及其预后至关重要。本研究旨在探讨IRGs在预测HCC预后中的临床价值。
我们从TCGA数据库下载了RNA测序数据和临床信息。样本被随机分为训练队列和测试队列。使用“limma”R包来识别HCC组和正常组之间差异表达的IRGs（DEIRGs）。通过单变量Cox分析获得预后DEIRGs（PDEIRGs）。使用LASSO和多变量Cox分析构建预后风险模型。为了更好地证明我们模型在预测总生存率方面的临床价值，构建了列线图。为了进一步研究我们模型的分子机制，进行了基因集富集分析（GSEA）。
与低风险组相比，高风险组的预后明显更差。此外，我们的预后风险模型可以准确地对肿瘤分级和TNM分期进行分层。重要的是，在我们的模型中，不仅免疫检查点基因得到了很好的预测，而且还揭示了人类白细胞抗原-I分子。GSEA表明“MAPK信号通路”、“mTOR信号通路”、“NOD样受体信号通路”、“Toll样受体信号通路”、“VEGF信号通路”、“WNT信号通路”与高风险组有显著相关性。
总体而言，我们的研究表明我们的预后风险模型可用于评估HCC的预后，这可能为HCC患者的生存率提供一定的依据。
